Brca2-specific modifier locus related to breast cancer risk

ABSTRACT

The present invention relates to novel SNP biomarkers and to panels of biomarkers that may be used in assessing the risk that a patient carrying a BRCA2 mutation will develop breast cancer. It is based, at least in part, on the identification of a SNP located at 6p24 in the human genome which is associated with breast cancer risk in subjects carrying the BRCA2 mutation but not in the general population. In specific non-limiting embodiments, a SNP from the 6p24 region may be used alone or together with one or more breast cancer risk biomarker to evaluate the likelihood that a subject will develop breast cancer.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Patent Application No. PCT/US14/032038, filed Mar. 27, 2014, which claims priority to U.S. Provisional Patent Application Ser. No. 61/805,783, filed Mar. 27, 2013, to both of which priority is claimed and the contents of both of which are incorporated herein in their entireties.

SEQUENCE LISTING

This application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Sep. 25, 2015, is named 072734.0156CON_SL.txt and is 51,560 bytes in size.

1. INTRODUCTION

The present invention relates to novel single nucleotide polymorphism (“SNP”) biomarkers and to panels of biomarkers that may be used in assessing the risk that a patient carrying a BRCA2 mutation will develop breast cancer. In particular non-limiting embodiments, the invention relates to biomarkers (minor alleles) in the human chromosome 6p24 region as indicators of decreased risk of developing breast cancer.

2. BACKGROUND OF THE INVENTION

The lifetime risk of breast cancer associated with carrying a BRCA2 mutation varies from 40 to 84% [1]. A genome-wide association study (“GWAS”) of BRCA2 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) was performed in order to determine whether common genetic variants modify breast cancer risk for BRCA2 mutation carriers [2]. Using the Affymetrix 6.0 platform, the discovery stage results were based on 899 young (<40 years) affected and 804 unaffected carriers of European ancestry. In a rapid replication stage wherein 85 discovery stage SNPs with the smallest P-values were genotyped in 2,486 additional BRCA2 mutation carriers, only published loci associated with breast cancer risk in the general population, including FGFR2 (10q26; rs2981575; P=1.2×10-8), were associated with breast cancer risk at the genome-wide significance level among BRCA2 mutation carriers. Two other loci, in ZNF365 (rs16917302) on 10q21 and a locus on 20q13 (rs311499), were also associated with breast cancer risk in BRCA2 mutation carriers with P-values <10-4 (P=3.8×10-5 and 6.6×10-5, respectively). A nearby SNP in ZNF365 was also associated with breast cancer risk in a study of unselected cases [3] and in a study of mammographic density [4]. Additional follow-up replicated the findings for rs16917302, but not rs311499 [5] in a larger set of BRCA2 mutation carriers. There remained a need to identify additional breast cancer risk modifying loci for BRCA2 mutation carriers.

3. SUMMARY OF THE INVENTION

The present invention relates to novel SNP biomarkers and to panels of biomarkers that may be used in assessing the risk that a patient carrying a BRCA2 mutation will develop breast cancer. It is based, at least in part, on the identification of a SNP located at 6p24 in the human genome which is associated with breast cancer risk in subjects carrying the BRCA2 mutation but not in the general population. In specific non-limiting embodiments, a SNP from the 6p24 region may be used alone or together with one or more breast cancer risk biomarker to evaluate the likelihood that a subject will develop breast cancer.

4. BRIEF DESCRIPTION OF THE FIGURES

FIG. 1. Associations between SNPs in the region surrounding rs9348512 on chromosome 6 and breast cancer risk for BRCA2 mutation carriers. Results based on imputed and observed genotypes. The blue spikes indicate the recombination rate at each position. Genotyped SNPs are represented by diamonds and imputed SNPs are represented by squares. Color saturation indicates the degree of correlation with the SNP rs9348512.

FIG. 2. Predicted breast cancer risks for BRCA2 mutation carriers by the combined SNP profile distributions of the known breast cancer susceptibility loci at FGFR2, TOX3, 12p11, 5q11, CDKN2A/B, LSP1, 8q24, ESR1, ZNF365, 3p24, 12q24, 5p12, 11q13 and the newly identified BRCA2 modifier locus at 6p24. The figure shows the risks at the 5th and 95th percentiles of the combined genotyped distribution as well as minimum, maximum and average risks.

FIG. 3A-C. Cluster plots for SNPs (A) rs9348512, (B) rs619373, and (C) rs184577.

FIG. 4A-B. Multidimensional scaling plots of the top two principal components of genomic ancestry of all eligible BRCA2 iCOGS samples plotted with the HapMap CEU, ASI, and YRI samples: (A) samples from Finland and BRCA2 6174delT carriers highlighted, and (B) samples, indicated in red, with >19% non-European ancestry were excluded.

FIG. 5A-C. Quantile-quantile plot comparing expected and observed distributions of P-values. Results displayed (A) for the complete sample, (B) after excluding samples from the GWAS discovery stage, and (C) for the complete sample and a set of SNPs from the iCOGS array that were selected independent from the results of the BRCA2 mutation carriers.

FIG. 6. Manhattan plot of P-values by chromosomal position for 18,086 SNPs selected on the basis of a previously published genome-wide association study of BRCA2 mutation carriers. Breast cancer associations results based on 4,330 breast cancer cases and 3,881 unaffected BRCA2 carriers.

FIG. 7. Forest plot of the country-specific, per-allele hazard ratios (HR) and 95% confidence intervals for the association between breast cancer and rs9348512 genotypes.

FIG. 8A-B. Forest plot of the country-specific, per-allele hazard ratios (HR) and 95% confidence intervals for the association with breast cancer for (A) rs619373 and (B) rs184577 genotypes.

5. DETAILED DESCRIPTION OF THE INVENTION

For clarity of disclosure and not by way of limitation the detailed description of the invention is divided into the following subsections:

(i) the BRCA2 Modifier Locus and its biomarkers;

(ii) risk assessment biomarker panels;

(iii) kits;

(iv) prognostic methods; and

(v) methods of treatment.

By way of introduction, the present invention relates to biomarkers which are allelic variations, allelic variants and/or single nucleotide polymorphisms. The biomarkers may be represented as nucleic acid molecules, for example SNPs, or may be represented as protein expression product of said nucleic acid.

The term “allelic variation” refers to the presence, in a population, of different forms of the same gene characterized by differences in their nucleotide sequences (sequences in genomic DNA). The variation may be in the form of one or more substitution, insertion, or deletion of a nucleotide. Different alleles may be functionally the same, or may be functionally different. In one subset of allelic variations, a single nucleotide is different between alleles and is referred to as a Single Nucleotide Polymorphism (“SNP”). Allelic variation in a known sequence may be identified by standard sequencing techniques. A “variation” or “variant,” as those terms are used herein, is relative to the ancestral gene found in the majority of the population. Unless specified otherwise, the presence of a SNP means that at the single nucleotide position for which alleles have been identified, the nucleotide present is the variant nucleotide (also referred to as the “minor allele”), not the nucleotide found in the majority of the population (also referred to as the “major allele”). The variation (variant) is comprised of a substituted nucleotide or nucleotides or an insertion or deletion of a nucleotide or nucleotides. Herein, generally the ancestral nucleotide (major allele) is listed first and the variation (variant, minor allele) nucleotide is listed second (for example, in A/G A is the ancestral nucleotide and G is the variation (variant) nucleotide). If there is an insertion, the ancestral nucleotide is represented by a hyphen (e.g., -/G). If there is a deletion, the variation (variant) nucleotide is represented by a hyphen (e.g., G/-). Numerous allelic variations (variants), captured in SNPs, of genes are known in the art and catalogued (for example, in the National Center for Biotechnology Information “Entrez SNP”). Allelic variations that are not SNPs include deletions or insertions or substitutions of multiple consecutive nucleotides.

In non-limiting embodiments of the invention, the presence of an allelic variation, for example a SNP, may be determined using a technique such as, but not limited to, primer extension or polymerase chain reaction, using primer(s) designed based on sequence in the proximity of the variation, followed by sequencing. For example, and not by way of limitation, the presence of a SNP may be determined by a method comprising using at least one primer sequence complementary to a sequence flanking the location of the SNP (for example, within 80 nucleotides, or within 50 nucleotides, or within 30 nucleotides, or within 20 nucleotides, or within 10 nucleotides, of the SNP) in a primer extension reaction or polymerase chain reaction to generate a test fragment that contains the location of the SNP and determining the nucleotide present at the location of the single nucleotide polymorphism, for example by sequencing all or a portion of the test fragment.

In addition to sequencing-based methods, other methods are known in the art for detecting one or more SNP including, but not limited to, methods that utilize hybridization, restriction fragment length polymorphism, enzyme-based methods, or electrophoresis, to name a few. Exemplary technologies for detecting SNPS include but are not limited to TaqMan® SNP Genotyping Assays, SNPlex™, Affymetrix Human SNP GeneChip (e.g. version 6.0), Sequenom MassArray sequencing, Illumina BeadArray products, and see, for example, De La Vega et al., 2005, Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 573:111-135; Larmy et al. 2006, Nucl. Acids Res. 34(14):e100; Shen et al., 2005, Mutat. Res. 573(1-2):70-82; Gaudet et al., 2009, Methods Mol. Biol. 578:415-424; Shen et al., 2009, Methods Mol. Biol. 578:293-306; Olivier, 2005, Mutat. Res. 573(1-2):103-110; Duan et al., 2009, Biosens. Bioelectron 24(7): 2095-2099; Duan et al., 2009, Nat. Protoc. 4(6):984-991; Hori et al., 2003, Curr. Pharm. Biotechnol. 4(6):477-484; Kwok and Chen, 2003, Curr. Issues Mol. Biol. 5:43-60; Kwok, 2002, Hum. Mutat. 19(4):315-323 and Comen et al., 2011, Breast Cancer Res. Treat. 127(2):479-87.

Nucleic acid sequences of certain SNPs and the surrounding nucleic acids are set forth in TABLE 7 below.

In non-limiting embodiments where a biomarker is a protein, methods standard to the art may be used to detect the protein biomarker. Such methods include, but are not limited to, electrophoretic or chromatographic methods, mass spectrometry techniques, peptide sequencing, 1-D or 2-D gel-based analysis systems, protein microarray, immunofluorescence, Western blotting, enzyme linked immunosorbent assay (“ELISA”), radioimmunoassay (RIA), enzyme immunoassays (EIA) and other antibody-mediated detection methods known in the art.

A “subject” herein is a human subject. In particular non-limiting embodiments, the subject is a female. In particular non-limiting embodiments, the subject has a family history of breast cancer, for example, a mother, sister, grandmother, or aunt with breast cancer, for example occurring before the age of 40 or before the age of 50.

5.1 the BRCA2 Modifier Locus and its Biomarkers

In various embodiments a SNP (variant, minor allele) in the 6p24 region (also referred to herein as a “6p24 SNP”) of the human genome may be used to assess the risk that a subject also carrying a BRCA2 mutation (variant, minor allele) may have or develop breast cancer, where the presence of the 6p24 SNP is associated with a lower risk that the subject has or will develop breast cancer relative to a subject having a BRCA2 mutation (also referred to as a BRCA2 biomarker) and the major allele at the 6p24 position. In certain non-limiting embodiments, the location of the SNP is between human chromosome 6 position 10540 kb and 10570 kb or between 10550 kb and 10565 kb or between 10560 kb and 10565 kb. In certain non-limiting embodiments, r² for the association between the SNP and breast cancer is at least 0.6 or at least 0.7 or at least 0.8.

In a specific non-limiting embodiment, the SNP is the minor allele at rs9348512, which, as illustrated in the working example below, was observed to be associated with a 15% decreased risk of breast cancer among BRCA2 mutation carriers (per allele HR=0.85, 95% CI 0.80-0.90) with no evidence of between-country heterogeneity (P=0.78, FIG. 7). The association with rs9348512 did not differ by 6174delT mutation status (P for difference=0.33), age (P=0.39), or estrogen receptor (ER) status of the breast tumor (P=0.78).

In a particular non-limiting embodiment, the presence of the major allele in a subject may be evaluated, for example as a means of determining heterozygosity or as a cross check when assessing whether the minor allele is present.

In other specific non-limiting embodiments, the SNP in the 6p24 region is a SNP listed in TABLE 5 below, where the minor alleles are shown. For example, but not by way of limitation, additional examples of 6p24 SNPs, which may be used according to the invention, include (minor alleles of) rs9358529, rs303067 and rs9366443.

5.2 Risk Assessment Biomarker Panels

A 6p24 SNP may be used alone or together with one or more additional biomarker (which together constitute a “panel”) to assess the risk that a subject has or will develop breast cancer.

As the association for the 6p24 SNP with breast cancer is in the context of a BRCA2 mutation, a BRCA2 mutation, for example, but not limited to a BRCA2 SNP, may optionally be included in the panel so that the existence of BRCA2 mutation and 6p24 SNP may be assessed in the same assay or series of assays.

In addition, the panel may further comprise one or more biomarker in one or more or two or more or three or more or four or more or five or more or six or more or seven or more or eight or more or nine or more or ten or more or eleven or more or twelve or thirteen of the following genes and/or loci (“auxiliary biomarkers” in that they are other than a 6p24 SNP or a BRCA2 biomarker): 10q26 (FGFR2), 16q12 (TOX3), 12p11 (PTHLH), 5q11 (MA P3K1), 9p21 (CDKN2A/B), 11p15 (LSP1), 8q24, 6q25 (ESR1), 10q21 (ZNF365), 3p24 (SLC4A7, NEK10), 12q24, 5p12, and 11 q13. A non-limiting set of SNPs corresponding to these biomarkers is provided in TABLES 1, 2, 5, and 6. TABLE 1 provides information regarding which biomarkers are associated with an increased risk of breast cancer and which biomarkers are associated with a decreased risk of breast cancer.

In certain non-limiting embodiments, a panel may comprise, in addition to a 6p24 SNP and optionally BRCA2, one or more biomarker selected from the biomarkers set forth in TABLES 1, 2, and 6.

In certain non-limiting embodiments, the panel may comprise, in addition to a 6p24 SNP and optionally BRCA2, one or both of the following biomarkers: 10q26 (FGFR2) and/or 16q12 (TOX3).

In certain non-limiting embodiments, the panel may comprise, in addition to a 6p24 SNP and optionally BRCA2, one, two, three, four, five or six of the following biomarkers: 10q26 (FGFR2), 16q12 (TOX3), 5q11 (MAP3K1), 11p15 (LSP1) and/or 3p24 (SLC4A7, NEK10).

In a specific non-limiting embodiment, a panel comprises a biomarker of 10q26 (FGFR2) which is the SNP rs2420946.

In a specific, non-limiting embodiment, a panel comprises a biomarker of 16q12 (TOX3) which is the SNP rs3803662.

In a specific non-limiting embodiment, a panel comprises a biomarker of 12p11 (PTHLH) which is the SNP rs27633.

In a specific non-limiting embodiment, a panel comprises a biomarker of 5q11 (MAP3K1) which is the SNP rs16886113.

In a specific non-limiting embodiment, a panel comprises a biomarker of 10q26 (CDKN2A/B) which is the SNP rs10965163.

In a specific non-limiting embodiment, a panel comprises a biomarker of 8q24 which is the SNP rs4733664.

In a specific non-limiting embodiment, a panel comprises a biomarker of 6q25 (ESR1) which is the SNP rs2253407.

In a specific non-limiting embodiment, a panel comprises a biomarker of 10q21 (ZNF365) which is the SNP rs17221319.

In non-limiting embodiments, the above-described panel of biomarkers may constitute at least 10% or at least 20% or at least 30% or at least 40% or at least 50% or at least 60% or at least 70% or at least 80% or at least 90% or at least 95% or 100% of the biomarkers tested in a panel.

Biomarkers, which are not SNPs, may be detected using methods known in the art. Materials for such methods are discussed in the kits section below.

5.3 Kits

In non-limiting embodiments, the present invention provides for a kit for determining whether a subject has an increased risk of having or developing breast cancer comprising a means for detecting a 6p24 SNP and one or more biomarker selected from BRCA2, and an auxiliary biomarker selected from the group of 10q26 (FGFR2), 16q12 (TOX3), 12p11 (PTHLH), 5q11 (MAP3K1), 9p21 (CDKN2A/B), 11p15 (LSP1), 8q24, 6q25 (ESR1), 10q21 (ZNF365), 3p24 (SLC4A7, NEK10), 12q24, 5p12, and 11q13.

Types of kits include, but are not limited to, packaged probe and primer sets (e.g. TaqMan probe/primer sets), arrays/microarrays, biomarker-specific antibodies and beads, which further contain one or more probes, primers, or other detection reagents for detecting one or more biomarkers of the present invention.

In a specific, non-limiting embodiment, a kit may comprise a pair of oligonucleotide primers, suitable for polymerase chain reaction (PCR) or nucleic acid sequencing, for detecting the biomarker(s) to be identified. A pair of primers may comprise nucleotide sequences complementary to a biomarker set forth above, and be of sufficient length to selectively hybridize with said biomarker. Alternatively, the complementary nucleotides may selectively hybridize to a specific region in close enough proximity 5′ and/or 3′ to the biomarker position to perform PCR and/or sequencing. Multiple biomarker-specific primers may be included in the kit to simultaneously assay large number of biomarkers. The kit may also comprise one or more polymerases, reverse transcriptase, and nucleotide bases, wherein the nucleotide bases can be further detectably labeled.

In non-limiting embodiments, a primer may be at least about 10 nucleotides or at least about 15 nucleotides or at least about 20 nucleotides in length and/or up to about 200 nucleotides or up to about 150 nucleotides or up to about 100 nucleotides or up to about 75 nucleotides or up to about 50 nucleotides in length.

In a specific, non-limiting embodiment, a kit may comprise at least one nucleic acid probe, suitable for in situ hybridization or fluorescent in situ hybridization, for detecting the biomarker(s) to be identified. Such kits will generally comprise one or more oligonucleotide probes that have specificity for various biomarkers.

In a further non-limiting embodiment, the oligonucleotide primers and/or probes may be immobilized on a solid surface or support, for example, on a nucleic acid microarray, wherein the position of each oligonucleotide primer and/or probe bound to the solid surface or support is known and identifiable.

In other non-limiting embodiments, a kit may comprise a primer for detection of a biomarker by primer extension.

In other non-limiting embodiments, a kit may comprise at least one antibody for immunodetection of the biomarker(s) to be identified. Antibodies, both polyclonal and monoclonal, specific for a biomarker, may be prepared using conventional immunization techniques, as will be generally known to those of skill in the art. The immunodetection reagents of the kit may include detectable labels that are associated with, or linked to, the given antibody or antigen itself. Such detectable labels include, for example, chemiluminescent or fluorescent molecules (rhodamine, fluorescein, green fluorescent protein, luciferase, Cy3, Cy5, or ROX), radiolabels (³H, ³⁵S, ³²P, ¹⁴C, ¹³¹I) or enzymes (alkaline phosphatase, horseradish peroxidase). Alternatively, a detectable moiety may be comprised in a secondary antibody or antibody fragment which selectively binds to the first antibody or antibody fragment (where said first antibody or antibody fragment specifically recognizes a biomarker).

In a further non-limiting embodiment, the biomarker-specific antibody may be provided bound to a solid support, such as a column matrix, an array, or well of a microtiter plate. Alternatively, the support may be provided as a separate element of the kit.

In one specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a 6p24 SNP biomarker.

In one specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting the rs9348512 SNP.

In certain non-limiting embodiments, a kit may comprise one or more pair of primers, primer, probe, microarray, or antibody suitable for detecting, in addition to a 6p24 SNP and optionally BRCA2, one, two, three, four, five or six of the following biomarkers: 10q26 (FGFR2), 16q12 (TOX3), 5q11 (MAP3K1), 11p15 (LSP1) and/or 3p24 (SLC4A7, NEK10).

In certain non-limiting embodiments, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting, in addition to a 6p24 SNP and optionally BRCA2, one or more of the biomarkers shown in TABLES 1, 2 and 6.

In a specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 10q26 (FGFR2) which is the SNP rs2420946.

In a specific, non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 16q12 (TOX3) which is the SNP rs3803662.

In a specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 12p11 (PTHLH) which is the SNP rs27633.

In a specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 5q11 (MAP3K1) which is the SNP rs16886113.

In a specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 10q26 (CDKN2A/B) which is the SNP rs10965163.

In a specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 8q24 which is the SNP rs4733664.

In a specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 6q25 (ESR1) which is the SNP rs2253407.

In a specific non-limiting embodiment, a kit may comprise a primer, a pair of primers, a probe, microarray, or antibody suitable for detecting a biomarker of 10q21 (ZNF365) which is the SNP rs17221319.

In certain non-limiting embodiments, where the measurement means in the kit employs an array, the set of biomarkers set forth above may constitute at least 10 percent or at least 20 percent or at least 30 percent or at least 40 percent or at least 50 percent or at least 60 percent or at least 70 percent or at least 80 percent of the species of markers represented on the microarray.

In certain non-limiting embodiments, a biomarker detection kit may comprise one or more detection reagents and other components (e.g., a buffer, enzymes such as DNA polymerases or ligases, chain extension nucleotides such as deoxynucleotide triphosphates, and in the case of Sanger-type DNA sequencing reactions, chain terminating nucleotides, positive control sequences, negative control sequences, and the like) necessary to carry out an assay or reaction to detect a biomarker.

A kit may further contain means for comparing the biomarker with a standard, and can include instructions for using the kit to detect the biomarker of interest. Specifically, the instructions describes that the presence of a biomarker, set forth herein, is indicative of an increased or decreased risk that a subject has or will develop breast cancer.

5.4 Prognostic Methods

In non-limiting embodiments, the present invention provides for a method for assessing the likelihood that a subject has or will develop breast cancer comprising determining whether the subject carries a 6p24 SNP biomarker and a BRCA2 biomarker, where the presence of both biomarkers indicates that while the subject has an increased risk of having or developing breast cancer relative to the general population, the risk is less than if the 6p24 biomarker were absent. In a non-limiting subset of embodiments, it is already known that the subject carries a BRCA2 biomarker (mutation, minor allele) so that the method need not necessarily re-test for the presence of that biomarker, although it may be desirable as confirmation or supporting information.

In non-limiting embodiments of said method, the 6p24 SNP biomarker is rs9348512 SNP.

In non-limiting embodiments, said method may further comprise determining whether the subject carries one or more auxiliary biomarker selected from the group of 10q26 (FGFR2), 16q12 (TOX3), 12p11 (PTHLH), 5q11 (MAP3K1), 9p21 (CDKN2A/B), 11p15 (LSP1), 8q24, 6q25 (ESR1), 10q21 (ZNF365), 3p24 (SLC4A7, NEK10), 12q24, 5p12, and 11q13. The effect of the presence or absence of said biomarker(s) on the risk of having or developing breast cancer is presented in TABLE 1.

In certain non-limiting embodiments, said method may further comprise determining whether the subject carries one or more biomarker selected from the biomarkers shown in TABLES 1, 2, and 6. The effect of the presence or absence of said biomarker(s) on the risk of having or developing breast cancer is presented in TABLES 1, 2, and/or 6.

The presence of a biomarker may be determined in the subject in vivo or in a sample collected from the subject using methods known in the art. Non-limiting examples of a sample include, but are not limited to, a clinical sample, a tumor sample, cells in culture, cell supernatants, lymphocytes, an exudate, cell lysates, serum, blood plasma, biological fluid (e.g., lymphatic fluid), and tissue samples. The source of the sample may be solid tissue (e.g., from a fresh, frozen, and/or preserved organ, tissue sample, biopsy, or aspirate), blood or any blood constituents, bodily fluids (such as, e.g., urine, lymph, cerebral spinal fluid, amniotic fluid, peritoneal fluid, saliva, or interstitial fluid), or cells from the individual, including circulating tumor cells.

Methods for determining the presence of biomarker are known in the art and are discussed above.

The foregoing method may comprise the further step, where the subject is found to carry a 6p24 SNP, of recommending or performing regular breast screening to monitor for the presence of cancer, for example by clinical breast exam, breast biopsy, mammography, ultrasound, magnetic resonance imaging, or similar techniques. Regular screening may, in non-limiting embodiments, be at least four times a year, at least twice a year, at least once a year, or every two years.

5.5 Methods of Treatment

In certain non-limiting embodiments, the present invention provides for a method of treating a subject who carries a BRCA2 biomarker (mutation), comprising determining whether the subject carries a 6p24 SNP biomarker and, where the 6p24 SNP biomarker is absent, advising the subject that she is at high risk for developing breast cancer relative to a subject carrying both the 6p24 SNP and BRCA2 biomarkers and to the general population. In a subset of non-limiting embodiments, the subject is previously known to carry the BR CA 2 biomarker (mutation); alternatively, both the 6p24 SNP and BRCA2 biomarkers may be assessed.

In non-limiting embodiments of said method, the 6p24 SNP biomarker is rs9348512 SNP.

In certain non-limiting embodiments, the method may further comprise determining whether the subject carries one or more auxiliary biomarker selected from the group of 10q26 (FGFR2), 16q12 (TOX3), 12p11 (PTHLH), 5q11 (MAP3K1), 9p21 (CDKN2A/B), 11p15 (LSP1), 8q24, 6q25 (ESR1), 10q21 (ZNF365), 3p24 (SLC4A7, NEK10), 12q24, 5p12 and 11q13. The effect of the presence or absence of said biomarker(s) on the risk of having or developing breast cancer is presented in TABLE 1.

In certain non-limiting embodiments, the method may further comprise determining whether the subject carries one or more biomarker selected from the biomarkers shown in TABLES 1, 2, and 6. The effect of the presence or absence of said biomarker(s) on the risk of having or developing breast cancer is presented in TABLES 1, 2, and/or 6.

The presence of a biomarker may be determined in the subject in vivo or in a sample collected from the subject using methods known in the art. Non-limiting examples of a sample include, but are not limited to, a clinical sample, a tumor sample, cells in culture, cell supernatants, lymphocytes, an exudate, cell lysates, serum, blood plasma, biological fluid (e.g., lymphatic fluid), and tissue samples. The source of the sample may be solid tissue (e.g., from a fresh, frozen, and/or preserved organ, tissue sample, biopsy, or aspirate), blood or any blood constituents, bodily fluids (such as, e.g., urine, lymph, cerebral spinal fluid, amniotic fluid, peritoneal fluid, saliva, or interstitial fluid), or cells from the individual, including circulating tumor cells.

Methods for determining the presence of biomarker are known in the art and are discussed above.

The foregoing method may comprise the further step, where the subject is found not to carry the 6p24 SNP, of recommending or performing a mastectomy or oophorectomy, or initiating anti-estrogen therapy or chemoprevention. In addition the biomarker profile may serve to guide targeted therapies against tyrosine kinase pathways which are implicated by many of the biomarkers included in the panel, and which may down-modulate risk in BRCA2 mutation carriers. The protein products of the biomarkers themselves may serve as therapeutic targets to down-modulate breast cancer risk.

6. EXAMPLE Identification of a BRCA2-Specific Modifier Locus at 6p24 Related to Breast Cancer Risk 6.1 Materials and Methods

Ethics Statement.

Each of the host institutions (TABLE 4) recruited under ethically-approved protocols. Written informed consent was obtained from all subjects.

Study Subjects.

The majority of BRCA2 mutation carriers were recruited through cancer genetics clinics and some came from population or community-based studies. Studies contributing DNA samples to these research efforts were members of the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) with the exception of one study (NICCC). Eligible subjects were women of European descent who carried a pathogenic BRCA2 mutation, had complete phenotype information, and were at least 18 years of age. Harmonized phenotypic data included year of birth, age at cancer diagnosis, age at bilateral prophylactic mastectomy and oophorectomy, age at interview or last follow-up, BRCA2 mutation description, self-reported ethnicity, and breast cancer estrogen receptor status.

GWAS Discovery Stage Samples.

Details of these samples have been described previously [2]. Data from 899 young (<40 years) affected and 804 older (>40 years) unaffected carriers of European ancestry from 14 countries were used to select SNPs for inclusion on the iCOGS array.

Samples Genotyped in the Extended Replication Set.

Forty-seven studies from 24 different countries (including two East-Asian countries) provided DNA from a total of 10,048 BRCA2 mutations carriers. All eligible samples were genotyped using COGs, including those from the discovery stage.

Genotyping and Quality Control.

-   -   BRCA2 SNP Selection for Inclusion on iCOGS.

The Collaborative Oncological Gene-Environment Study (COGS) consortium developed a custom genotyping array (referred to as the iCOGS array) to provide efficient genotyping of common and rare genetic variants to identify novel loci that are associated with risk of breast, ovarian, and prostate cancers as well as to fine-map known cancer susceptibility loci. SNPs were selected for inclusion on iCOGS separately by each participating consortium: Breast Cancer Association Consortium (BCAC) [6], Ovarian Cancer Association Consortium (OCAC) [7], Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) [8], and CIMBA. SNP lists from a BRCA1 GWAS and SNPs in candidate regions were used together with the BRCA2 GWAS lists to generate a ranked CIMBA SNP list that included SNPs with the following nominal proportions: 55.5% from the BRCA1 GWAS, 41.6% from the BRCA2 GWAS and fine mapping, 2.9% for CIMBA candidate SNPs. Each consortium was given a share of the array: nominally 25% of the SNPs each for BCAC, PRACTICAL and OCAC; 17.5% for CIMBA; and 7.5% for SNPs from commonly researched pathways (e.g., inflammation). For the CIMBA BRCA2 GWAS, we used the iCOGS array as the platform to genotype the extended replication set of the discovery GWAS stage [2]. SNPs were selected on the basis of the strength of their associations with breast cancer risk in the discovery stage [2], using imputed genotype data for 1.4M SNPs identified through CEU+TSI samples on HapMap3, release 2. A ranked list of SNPs was based on the 1-df trend test statistic, after excluding highly correlated SNPs (r2>0.4). The final list included the 39,015 SNPs with the smallest p-values. An additional set of SNPs were selected for fine mapping of the regions surrounding the SNPs found to be associated with breast cancer in the discovery GWAS stage: rs16917302 on 10q21 and rs311499 on 20q13, including SNPs with a MAF >0.05 located 500 kb in both directions of the SNP, based on HapMap 2 data. The final combined list of SNPs for the iCOGS array comprised 220,123 SNPs. Of these, 211,155 were successfully manufactured onto the array. The present analyses are based on the 19,029 SNPs selected on the basis of BRCA2 GWAS and fine mapping that were included on the iCOGS array.

-   -   Genotyping.

The genotyping was performed on DNA samples from 10,048 BRCA2 mutation carriers at the McGill University and Genome Québec Innovation Centre (Montreal, Canada). As a quality control measure, each plate included DNA samples from six individuals who were members of two CEPH trios. Some plates also contained three duplicate pairs of quality control samples. Genotypes were called using GenCall [9]. Initial calling was based on a cluster file generated using 270 samples from Hapmap2. To generate the final calls, we first selected a subset of 3,018 individuals, including samples from each of the genotyping centers in the iCOGS project, each of the participating consortia, and each major ethnicity. Only plates with a consistent high call rate in the initial calling were used. We also included 380 samples of European, African, and Asian ethnicity genotyped as part of the Hapmap and 1000 Genomes project, and 160 samples that were known positive controls for rare variants on the array. This subset was used to generate a cluster file that was then applied to call the genotypes for the remaining samples.

Quality Control of SNPs.

Of the 211,155 SNPs on the iCOGS array, we excluded SNPs for the following reasons (TABLE 4): on the Y-chromosome, call rate <95%, deviations from Hardy-Weinberg equilibrium (P<10-7) using a stratified 1-d.f. test [10], and monomorphic. SNPs that gave discrepant genotypes among known duplicates were also excluded. After quality control filtering, 200,908 SNPs were available for analysis (TABLE 3); 18,086 of which were selected on the basis of the discovery BRCA2 GWAS [2]. Cluster plots of all reported SNPs were inspected manually for quality (FIG. 3).

Description of Imputation.

Genotypes for SNPs identified through the 1000 Genomes Phase I data (released January 2012) [11] were imputed using all SNPs on the iCOGS chip in a region of 500 kb around the novel modifier locus at 6p24. The boundaries were determined according to the linkage disequilibrium (LD) structure in the region based on HapMap data. The imputation was carried out using IMPUTE 2.2 [12]. SNPs with imputation information/accuracy r²<0.30 were excluded in the analyses.

Quality Control of DNA Samples.

Of 10,048 genotyped samples (TABLE 3), 742 were excluded because they did not meet the phenotypic eligibility criteria or had self-reported non-CEU ethnicity (TABLE 4). Samples were then excluded for the following reasons: not female (XXY, XY), call rate <95%, low or high heterozygosity (P<10-6), discordant genotypes from previous CIMBA genotyping efforts, or discordant duplicate samples. For duplicates with concordant phenotypic data, or in cases of cryptic monozygotic twins, only one of the samples was included. Cryptic duplicates for which phenotypic data indicated different individuals were all excluded. Samples of non-European ancestry were identified using multi-dimensional scaling, after combining the BRCA2 mutation carrier samples with the HapMap2 CEU, CHB, JPT and YRI samples using a set of 37,120 uncorrelated SNPs from the iCOGS array. Samples with >19% non-European ancestry were excluded (FIG. 4). A total of 4,330 affected and 3,881 unaffected BRCA2 mutation carrier women of European ancestry from 42 studies remained in the analysis (TABLE 4), including 3,234 breast cancer cases and 3,490 unaffected carriers that were not in the discovery set.

BRCA1 and BCAC Samples.

Details of the sample collection, genotyping and quality control process for the BRCA1 and BCAC samples, are reported elsewhere [13,14].

Statistical Methods.

The associations between genotype and breast cancer risk were analyzed within a retrospective cohort framework with time to breast cancer diagnosis as the outcome [15]. Each BRCA2 carrier was followed until the first of: breast or ovarian cancer diagnosis, bilateral prophylactic mastectomy, or age at last observation. Only those with a breast cancer diagnosis were considered as cases in the analysis. The majority of mutation carriers were recruited through genetic counseling centers where genetic testing is targeted at women diagnosed with breast or ovarian cancer and in particular to those diagnosed with breast cancer at a young age. Therefore, these women are more likely to be sampled compared to unaffected mutation carriers or carriers diagnosed with the disease at older ages. As a consequence, sampling was not random with respect to disease phenotype and standard methods of survival analysis (such as Cox regression) may lead to biased estimates of the associations [16]. We therefore conducted the analysis by modelling the retrospective likelihood of the observed genotypes conditional on the disease phenotypes. This has been shown to provide unbiased estimates of the associations [15]. The implementation of the retrospective likelihoods has been described in detail elsewhere [15,17]. The associations between genotype and breast cancer risk were assessed using the 1 degree of freedom score test statistic based on the retrospective likelihood [15]. In order to account for non-independence between relatives, an adjusted version of the score test was used in which the variance of the score was derived taking into account the correlation between the genotypes [18]. P-values were not adjusted using genomic control because there was little evidence of inflation. Inflation was assessed using the genomic inflation factor λ. Since this estimate is dependent on sample size, we also calculated λ adjusted to 1000 affected and 1000 unaffected samples. Per-allele and genotype-specific hazard-ratios (HR) and 95% confidence intervals (CI) were estimated by maximizing the retrospective likelihood. Calendar-year and cohort-specific breast cancer incidences for BRCA2 were used [1]. All analyses were stratified by country of residence. The USA and Canada strata were further subdivided by reported Ashkenazi Jewish ancestry. The assumption of proportional hazards was assessed by fitting a model that includes a genotype-by-age interaction term. Between-country heterogeneity was assessed by comparing the results of the main analysis to a model with country-specific log-HRs. A possible survival bias due to inclusion of prevalent cases was evaluated by re-fitting the model after excluding affected carriers that were diagnosed ≧5 years prior to study recruitment. The associations between genotypes and tumor subtypes were evaluated using an extension of the retrospective likelihood approach that models the association with two or more subtypes simultaneously [19]. To investigate whether any of the significant SNPs were associated with ovarian cancer risk for BRCA2 mutation carriers and whether the inclusion of ovarian cancer patients as unaffected subjects biased our results, we also analyzed the data within a competing risks framework and estimated HR simultaneously for breast and ovarian cancer using the methods described elsewhere [15]. Analyses were carried out in R using the GenABEL libraries [20] and custom-written software. The retrospective likelihood was modeled in the pedigree-analysis software MENDEL [21], as described in detail elsewhere [15].

TCGA Analysis. Affymetrix SNP 6.0 genotype calls for normal (non-tumor) breast DNA were downloaded for all available individuals from The Cancer Genome Atlas in September 2011. Analyses were limited to the 401 individuals of European ancestry based on principal component analysis. Expression levels in breast tumor tissue were adjusted for the top two principal components, age, gender (there are some male breast cancer cases in TCGA), and average copy number across the gene in the tumor. Linear regression was then used to test for association between the SNP and the adjusted gene expression level for all genes within one megabase.

Gene Set Enrichment Analysis.

To investigate enrichment of genes associated with breast cancer risk, the gene-set enrichment approach was implemented using Versatile Gene-based Association Study [22] based on the ranked P-values from retrospective likelihood analysis. Association List Go Annotator was also used to prioritize gene pathways using functional annotation from gene ontology (GO) [23] to increase the power to detect association to a pathway, as opposed to individual genes in the pathway. Both analyses were corrected for LD between SNPs, variable gene size, and interdependence of GO categories, where applicable, based on imputation. 100,000 Monte Carlo simulations were performed in VEGAS and 5000 replicate gene lists using random sampling of SNPs and 5000 replicate studies (sampling with replacement) were performed to estimate P-values.

Predicted Absolute Breast Cancer Risks by Combined SNP Profile.

We estimated the absolute risks of developing breast cancer based on the joint distribution of SNPs associated with breast cancer for BRCA2 mutation carriers. The methods have been described elsewhere [24]. To construct the SNP profiles, we considered the single SNP from each region with the strongest evidence of association in the present dataset. We included all loci that had previously been found to be associated with breast cancer risk through GWAS in the general population and demonstrated associations with breast cancer risk for BRCA2 mutation carriers, and loci that had GWAS level of significance in the current study. We assumed that all loci in the profile were independent (i.e., they interact multiplicatively on BRCA2 breast cancer risk). Genotype frequencies were obtained under the assumption of Hardy-Weinberg Equilibrium. For each SNP, the effect of each allele was assumed to be consistent with a multiplicative model (log-additive). We assumed that the average, age-specific breast cancer incidences, over all associated loci, agreed with published breast cancer risk estimates for BRCA2 mutation carriers [1].

6.2 Results

The genomic inflation factor (λ) based on the 18,086 BRCA2 GWAS SNPs in the 6,724 BRCA2 mutation carriers not used for SNP discovery was 1.034 (λ adjusted to 1000 affected and 1000 unaffected: 1.010, FIG. 5). Multiple variants were associated with breast cancer risk in the combined discovery and replication datasets (FIG. 6). SNPs in three independent regions had P-values <5×10⁻⁸; one was a region not previously associated with breast cancer.

The most significant associations were observed for known breast cancer susceptibility regions, rs2420946 (per allele P=2×10⁻¹⁴) in FGFR2 and rs3803662 (P=5.4×10⁻¹¹) near TOX3 (TABLE 1). Breast cancer risk associations with other SNPs reported previously for BRCA2 mutation carriers are summarized in TABLE 1. In this larger set of BRCA2 mutation carriers, we also identified novel SNPs in the 12p11 (PTHLH), 5q11 (MAP3K1), and 9p21 (CDKN2A/B) regions with smaller P-values for association than those of previously reported SNPs. These novel SNPs were not correlated with the previously reported SNPs (r²<0.14). For one of the novel SNPs identified in the discovery GWAS [2], ZNF365 rs16917302, there was weak evidence of association with breast cancer risk (P=0.01); however, an uncorrelated SNP, rs17221319 (r²<0.01), 54 kb upstream of rs16917302, had stronger evidence of association (P=6×10-3).

One SNP, rs9348512 at 6p24, not known to be associated with breast cancer, had a combined P-value of association of 3.9×10⁻⁸ amongst all BRCA2 samples (TABLE 2), with strong evidence of replication in the set of BRCA2 samples that were not used in the discovery stage (P=5.2×10⁻⁵). The minor allele of rs9348512 (MAF-0.35) was associated with a 15% decreased risk of breast cancer among BRCA2 mutation carriers (per allele HR=0.85, 95% CI 0.80-0.90) with no evidence of between-country heterogeneity (P=0.78, FIG. 7). None of the genotyped (n=68) or imputed (n=3,507) SNPs in that region showed a stronger association with risk (FIG. 1; TABLE 5), but there were 40 SNPs with P<10⁻⁴ (pairwise r²>0.38 with rs9348512, with the exception of rs11526201 for which r²=0.01, TABLE 5). The association with rs9348512 did not differ by 6174delT mutation status (P for difference=0.33), age (P=0.39), or estrogen receptor (ER) status of the breast tumor (P=0.78). Exclusion of prevalent breast cancer cases (n=1,752) produced results (HR=0.83, 95% CI 0.77-0.89, P=3.40×10⁻⁷) consistent with those for all cases.

SNPs in two additional regions had P-values <10⁻⁵ for breast cancer risk associations for BRCA2 mutation carriers (TABLE 2). The magnitude of associations for both SNPs was similar in the discovery and second stage samples. In the combined analysis of all samples, the minor allele of rs619373, located in FGF13 (Xq26.3), was associated with higher breast cancer risk (HR=1.30, 95% CI 1.17-1.45, P=3.1×10-6). The minor allele of rs184577, located in CYP1B1-AS1 (2p22-p21), was associated with lower breast cancer risk (HR=0.85, 95% CI 0.79-0.91, P=3.6×10-6). These findings were consistent across countries (P for heterogeneity between country strata=0.39 and P=0.30, respectively; FIG. 8). There was no evidence that the HR estimates for rs619373 and rs184577 change with age of the BRCA2 mutation carriers (P for the genotype-age interaction=0.80 and P=0.40, respectively) and no evidence of survival bias for either SNP (rs619373: HR=1.35, 95% CI 1.20-1.53, P=1.5×10-6 and rs184577: HR=0.86, 95% CI 0.79-0.93, P=2.0×10-4, after excluding prevalent cases). The estimates for risk of ER-negative and ER-positive breast cancer were not significantly different (P for heterogeneity between tumor subtypes=0.79 and 0.67, respectively). When associations were evaluated under a competing risks model, there was no evidence of association with ovarian cancer risk for SNPs rs9348512 at 6p24, rs619373 in FGF13 or rs184577 at 2p22 and the breast cancer associations were virtually unchanged (TABLE 6).

Gene set enrichment analysis confirmed that strong associations exist for known breast cancer susceptibility loci and the novel loci identified here (gene-based P<1×10⁻5). The pathways most strongly associated with breast cancer risk that contained statistically significant SNPs included those related to ATP binding, organ morphogenesis, and several nucleotide bindings (pathway-based P<0.05).

To begin to determine the functional effect of rs9348512, we examined associations of expression levels of any nearby gene in breast tumors with the minor A allele. Using data from The Cancer Genome Atlas, we found that the A allele of rs9348512 was strongly associated with mRNA levels of GCNT2 in breast tumors (p=7.3×10⁻⁵).

6.3 Discussion

In the largest assemblage of BRCA2 mutation carriers, we identified a novel locus at 6q24 that is associated with breast cancer risk, and noted two potential SNPs of interest at Xq26 and 2p22. We also replicated associations with known breast cancer susceptibility SNPs previously reported in the general population and in BRCA2 mutation carriers. For the 12p11 (PTHLH), 5q11 (MAP3K1), and 9p21 (CDKN2A/B), we found uncorrelated SNPs that had stronger associations than the originally identified SNP in the breast cancer susceptibility region that should be replicated in the general population. In BRCA2 mutation carriers, evidence for a breast cancer association with genetic variants in PTHLH has been restricted previously to ER-negative tumors [25]; however, the novel susceptibility variant we reported here was associated with risk of ER+ and ER− breast cancer.

The novel SNP rs9348512 (6p24) is located in a region with no known genes (FIG. 1). C6orf218, a gene encoding a hypothetical protein LOC221718, and a possible tumor suppressor gene, TFAP2A, are within 100 kb of rs9348512. TFAP2A encodes the AP-2α transcription factor that is normally expressed in breast ductal epithelium nuclei, with progressive expression loss from normal, to ductal carcinoma in situ, to invasive cancer [26,27]. AP-2α also acts as a tumor suppressor via negative regulation of MYC [28] and augmented p53-dependent transcription [29]. However, the minor allele of rs9348512 was not associated with gene expression changes of TFAP2A in breast cancer tissues in The Cancer Genome Atlas (TCGA) data; this analysis might not be informative since expression of TFAP2A in invasive breast tissue is low [26,27]. Using the TCGA data and a 1 Mb window, expression changes with genotypes of rs9348512 were observed for GCNT2, the gene encoding the enzyme for the blood group 1 antigen glucosaminyl (N-acetyl) transferase 2. GCNT2, recently found to be overexpressed in highly metastatic breast cancer cell lines [30] and basal-like breast cancer [31], interacts with TGF-β to promote epithelial-to-mesenchymal transition, enhancing the metastatic potential of breast cancer [31]. An assessment of alterations in expression patterns in normal breast tissue from BRCA2 mutation carriers by genotype are needed to further evaluate the functional implications of rs9348512 in the breast tumorigenesis of BRCA2 mutation carriers.

To determine whether the breast cancer association with rs9348512 was limited to BRCA2 mutation carriers, we compared results to those in the general population genotyped by BCAC and to BRCA1 mutation carriers in CIMBA. No evidence of an association between rs9348512 and breast cancer risk was observed in the general population (OR=1.00, 95% CI 0.98-1.02, P=0.74) [14], nor in BRCA1 mutation carriers (HR=0.99, 95% CI 0.94-1.04, P=0.75) [13]. Stratifying cases by ER status, there was no association observed with ER-subtypes in either the general population or among BRCA1 mutation carriers (BCAC: ER positive P=0.89 and ER negative P=0.60; CIMBA BRCA1: P=0.49 and P=0.99, respectively). For the two SNPs associated with breast cancer with P<10-5, neither rs619373, located in FGF13 (Xq26.3), nor rs184577, located in CYP1B1-AS1 (2p22-p21), was associated with breast cancer risk in the general population [14] or among BRCA1 mutation carriers [13]. The narrow CIs for the overall associations in the general population and in BRCA1 mutation carriers rule out associations of magnitude similar to those observed for BRCA2 mutation carriers. The consistency of the association in the discovery and replication stages and by country, the strong quality control measures and filters, and the clear cluster plot for rs9348512 suggest that our results constitute the discovery of a novel breast cancer susceptibility locus specific to BRCA2 mutation carriers rather than a false positive finding.

rs9348512 (6p24) is the first example of a common susceptibility variant identified through GWAS that modifies breast cancer risk specifically in BRCA2 mutation carriers. Previously reported BRCA2-modifying alleles for breast cancer, including those in FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, 1p11.2, ZNF365, and 19p13.1 (ER-negative only) [18,32,33], are also associated with breast cancer risk in the general population and/or BRCA1 mutation carriers.

Taking into account all loci associated with breast cancer risk in BRCA2 mutation carriers from the current analysis, including the 6p24 locus, the 5% of the BRCA2 mutation carriers at lowest risk were predicted to have breast cancer risks by age 80 in the range of 21-47% compared to 83-100% for the 5% of mutation carriers at highest risk on the basis of the combined SNP profile distribution (FIG. 2). The breast cancer risk by age 50 is predicted to be 4-11% for the 5% of the carriers at lowest risk compared to 29-81% for the 5% at highest risk.

TABLE 1 Per allele hazard ratios (HR) and 95% confidence intervals (CI) of previously published breast cancer loci among BRCA2 mutation carriers from previous reports and from the iCOGS array, ordered by statistical significance of the region Previously Reported Results Chr Report Minor Affected Unaffected Per Allele p- (Nearby Genes) Status¹ SNP r² Allele Ref N N HR (95% CI) value² 10q26 reported rs2981575 0.96 G [2] 2,155 2,016 1.28 (1.18, 1.39) 1 × 10⁻⁸ (FGFR2) novel rs2420946 A 16q12 reported rs3803662 A [2] 2,162 2,026 1.20 (1.10, 1.31) 5 × 10⁻⁵ (TOX3) 12p11 reported rs10771399 0.05 G [34] 3,798 3,314 0.93 (0.84, 1.04) 0.20 (PTHLH) novel rs27633 C 5q11 reported rs889312 0.14 C [24] 2,840 2,282 1.10 (1.01, 1.19) 0.02 (MAP3K1) novel rs16886113 C 9p21 reported rs1011970 0.00 A [34] 3,807 3,316 1.09 (1.00, 1.18) 0.05 (CDKN2A/B) novel rs10965163 A 11p15 reported rs3817198 G [24] 3,266 2,636 1.14 (1.06, 1.23) 8 × 10⁻⁴ (LSP1) 8q24 reported rs13281615 0.00 G [24] 3,338 2,723 1.06 (0.98, 1.13) 0.13 novel rs4733664 A 20q13 reported rs311498³ 0.00 A⁴ [5] 3,808 3,318 0.95 (0.84, 1.07) 0.36 novel rs13039229 C 6q25 reported rs9397435 0.01 G [35] 3,809 3,316 1.14 (1.01, 1.27) 0.03 (ESR1) novel rs2253407 A 10q21 reported rs16917302 0.00 C [5] 3,807 3,315 0.83 (0.75, 0.93) 7 × 10⁻⁴ (ZNF365) novel rs17221319 A 3p24 reported rs4973768 A [24] 3,370 2,783 1.10 (1.03, 1.18) 6 × 10⁻³ (SLC4A7, NEK10) 12q24 reported rs1292011⁴ G [34] 2,530 2,342 0.94 (0.87, 1.01) 0.10 5p12 reported rs10941679⁴ G [24] 3,263 2,591 1.09 (1.01, 1.19) 0.03 11q13 reported rs614367 A [34] 3,789 3,307 1.03 (0.95, 1.13) 0.46 1p11 reported rs11249433 G [35] 3,423 2,827 1.09 (1.02, 1.17) 0.02 (NOTCH2) 17q23 reported rs6504950 A [24] 3,401 2,813 1.03 (0.95, 1.11) 0.47 (STXBP4, COX11) 19p13 reported rs8170 A [5] 3,665 3,086 0.98 (0.90, 1.07) 0.66 (MERIT40) 2q35 reported rs13387042⁴ G [24] 3,300 2,646 1.05 (0.98, 1.13) 0.14 9q31 reported rs865686 C [34] 3,799 3,312 0.95 (0.89, 1.01) 0.10 10q22 reported rs704010 A [34] 3,761 3,279 1.01 (0.95, 1.08) 0.73 (ZMIZ1) iCOGS Results Chr Report Minor Affected Unaffected Per Allele (Nearby Genes) Status¹ SNP r² Allele N N MAF HR (95% CI) p-value² 10q26 reported rs2981575 0.96 G 4,326 3,874 0.40 1.25 (1.18, 1.33)  2 × 10⁻¹³ (FGFR2) novel rs2420946 A 4,328 3,877 0.39 1.27 (1.19, 1.34)  2 × 10⁻¹⁴ 16q12 reported rs3803662 A 4,330 3,880 0.27 1.24 (1.16, 1.32)  5 × 10⁻¹¹ (TOX3) 12p11 reported rs10771399 0.05 G 4,330 3,880 0.11 0.89 (0.81, 0.98) 0.02 (PTHLH) novel rs27633 C 4,252 3,841 0.39 1.14 (1.07, 1.21) 4 × 10⁻⁵ 5q11 reported rs889312 0.14 C 4,330 3,881 0.29 1.04 (0.98, 1.11) 0.20 (MAP3K1) novel rs16886113 C 4.330 3,881 0.06 1.24 (1.11, 1.38) 1 × 10⁻⁴ 9p21 reported rs1011970 0.00 A 4,330 3,881 0.17 1.03 (0.95, 1.11) 0.51 (CDKN2A/B) novel rs10965163 A 4,329 3,880 0.10 0.84 (0.77, 0.93) 8 × 10⁻⁴ 11p15 reported rs3817198 G 4,316 3,870 0.33 1.11 (1.04, 1.18) 9 × 10⁻⁴ (LSP1) 8q24 reported rs13281615 0.00 G 4,248 3,810 0.43 1.03 (0.97, 1.09) 0.31 novel rs4733664 A 4,329 3,879 0.41 1.10 (1.04, 1.17) 2 × 10⁻³ 20q13 reported rs311498³ 0.00 A⁴ 4.330 3,880 0.07 0.95 (0.84, 1.06) 0.31 novel rs13039229 C 4,326 3,877 0.21 0.90 (0.84, 0.97) 5 × 10⁻³ 6q25 reported rs9397435 0.01 G 4,330 3,881 0.08 1.12 (1.00, 1.25) 0.03 (ESR1) novel rs2253407 A 4,330 3,881 0.47 0.92 (0.86, 0.98) 5 × 10⁻³ 10q21 reported rs16917302 0.00 C 4,330 3,881 0.11 0.88 (0.80, 0.98) 0.01 (ZNF365) novel rs17221319 A 4,330 3,881 0.46 1.09 (1.02, 1.15) 6 × 10⁻³ 3p24 reported rs4973768 A 4,322 3,875 0.49 1.09 (1.02, 1.15) 7 × 10⁻³ (SLC4A7, NEK10) 12q24 reported rs1292011⁴ G 4.313 3.875 0.42 0.92 (0.87, 0.98) 0.01 5p12 reported rs10941679⁴ G 4,320 3,875 0.24 1.07 (1.01, 1.15) 0.04 11q13 reported rs614367 A 4,330 3,880 0.14 1.08 (1.00, 1.17) 0.04 1p11 reported rs11249433 G 4,328 3,881 0.40 1.05 (0.99, 1.12) 0.10 (NOTCH2) 17q23 reported rs6504950 A 4,329 3,881 0.26 1.04 (0.97, 1.11) 0.23 (STXBP4, COX11) 19p13 reported rs8170 A 4,327 3,876 0.19 0.98 (0.91, 1.06) 0.62 (MERIT40) 2q35 reported rs13387042⁴ G 4,326 3,880 0.48 0.99 (0.93, 1.05) 0.66 9q31 reported rs865686 C 4,330 3,880 0.36 0.99 (0.93, 1.05) 0.77 10q22 reported rs704010 A 4.328 3.878 0.38 1.01 (0.95, 1.07) 0.91 (ZMIZ1) ¹Reporting status of the SNP is either previously reported or novel to this report. ²p-value was calculated based on the 1-degree of freedom score test statistic. ³rs311499 could not be designed onto the iCOGS array. A surrogate (r² = 1.0), rs311498, was included, however, and reported here. ⁴Stronger associations were originally reported for the SNP, assuming a dominant or recessive model of the ‘risk allele’.

TABLE 2 Breast cancer hazard ratios (HR) and 95% confidence intervals (CI) of novel breast cancer loci with P-values of association <10⁻⁵ among BRCA2 mutation carriers SNP rs No. Discovery Stage Stage 2 Chr. Affected Unaffected Affected Unaffected (Nearby No. No. No. No. Genes) Genotype (%) (%) MAF HR (95% CI) p-value¹ (%) (%) MAF HR (95% CI) rs93485126 CC 390 248 0.39 1.00 1,606 1392 0.35 1.00 (TFAP2A (46.4) (38.3) (46.0) (43.0) C6orf218) CA 368 299 0.81 (0.67-0.96) 1515 1432 0.92 (0.83-1.01) (43.8) (46.2) (43.4) (44.3) AA 82 100 0.55 (0.42-0.74) 368 410 0.72 (0.62-0.84) (9.8) (15.5) (10.5) (12.7) per allele 0.76 (0.67-0.87) 2.6 × 10⁻⁵ 0.87 (0.81-0.93) rs619373 X GG 693 568 0.06 1.00 2882 2784 0.07 1.00 (FGF13) (75.8) (87.8) (82.7) (86.1) GA 143 78 1.43 (1.13-1.80) 583 439 1.25 (1.10-1.43) (15.7) (12.1) (16.7) (13.6) AA 4 1 2.01 (0.50-8.06) 21 11 2.09 (1.09-4.03) (8.5) (0.1) (0.6) (0.3) per allele 1.43 (1.15-1.78) 3.0 × 10⁻³ 1.27 (1.12-1.44) rs1845772 GG 520 368 0.25 1.00 2104 1824 0.25 1.00 (C2orf58) (61.9) (56.9) (60.3) (56.4) GA 278 234 0.86 (0.71-1.03) 1212 1231 0.83 (0.75-0.92) (33.1) (36.2) (34.7) (38.1) AA 42 45 0.67 (0.46-0.96) 174 179 0.80 (0.64-0.99) (5.0) (7.0) (5.0) (5.5) per allele 0.84 (0.73-0.97) 1.5 × 10⁻² 0.86 (0.79-0.93) SNP rs No. Combined Chr. Affected Unaffected (Nearby Stage 2 No. No. Genes) Genotype p-value¹ (%) (%) MAF HR (95% CI) p-value¹ rs93485126 CC 1,996 1640 0.35 1.00 (TFAP2A (46.1) (42.3) C6orf218) CA 1883 1731 0.89 (0.82-0.97) (43.5) (44.6) AA 450 510 0.68 (0.59-0.78) (10.4) (12.1) per allele 5.2 × 10⁻⁵ 0.85 (0.80-0.90) 3.9 × 10⁻⁸ rs619373 X GG 3575 3352 0.07 1.00 (FGF13) (82.6) (86.4) GA 726 517 1.29 (1.15-1.45) (16.8) (13.3) AA 25 12 1.99 (1.16-3.41) (0.6) (0.3) per allele 2.0 × 10⁻⁴ 1.30 (1.17-1.45) 3.1 × 10⁻⁶ rs1845772 GG 2624 2192 0.25 1.00 (C2orf58) (60.6) (56.5) GA 1490 1465 0.83 (0.76-0.91) (34.4) (37.8) AA 216 224 0.77 (0.64-0.93) (5.0) (5.8) per allele 8.6 × 10⁻⁵ 0.85 (0.79-0.91) 3.6 × 10⁻⁶ ¹P-value was calculated based on the 1-degree of freedom score test

TABLE 3 Description of breast cancer affected and unaffected BRCA2 carriers included in the final analysis of the COGs array SNPs Affected Unaffected (n = 4,330) (n = 3,881) Factor N % N % Age at Censoring <40 1,545 35.7 1,607 41.4 40-49 1,651 38.1 1,025 26.4 50-59 799 18.5 712 18.4 60+ 335 7.7 537 13.8 Ashkenazi Jewish Ancestry No 3,988 92.1 3,433 88.5 Yes 342 7.9 448 11.5 BRCA2*617delT Mutation Carrier Yes 435 10.0 584 15.0 No 3,895 90.0 3,297 85.0 Country of Residence Australia 288 6.7 200 5.2 Austria 123 2.8 77 2.0 Canada 153 3.5 150 3.9 Denmark & Sweden 158 3.6 198 5.1 Finland 66 1.5 55 1.4 France 491 11.3 209 5.4 Germany 365 8.4 198 5.1 Iceland 102 2.4 25 0.6 Israel 108 2.5 166 4.3 Italy 353 8.2 174 4.5 South Africa 93 2.1 53 1.4 Spain 328 7.6 293 7.5 The Netherlands 260 6.0 492 12.7 United Kingdom & 483 11.2 560 14.4 Ireland USA 959 22.1 1,031 26.6 Study BCFR 197 4.5 152 3.9 BIDMC 4 0.09 5 0.1 BMBSA 93 2.1 53 1.4 BRICOH 48 1.1 80 2.1 CBCS 46 1.1 49 1.3 CNIO 113 2.6 113 2.9 COH 65 1.5 42 1.1 CONSIT TEAM 263 6.1 137 3.5 DFCI 55 1.3 79 2.0 DKFZ 14 0.3 11 0.3 EMBRACE 478 11.0 547 14.1 FCCC 19 0.4 35 0.9 GC-HBOC 351 8.1 186 4.8 GEMO 523 12.1 226 5.8 GOG 152 3.5 161 4.1 HCSC 59 1.4 54 1.4 HEBON 260 6.0 492 12.7 HEBCS 66 1.5 55 1.4 HVH 34 0.8 25 0.6 ICO 122 2.8 102 2.6 ILUH 103 2.4 26 0.7 INHERIT 26 0.6 23 0.6 IOVHBOCS 90 2.1 37 1.0 kConFab 254 5.9 182 4.7 MAGIC 8 0.2 22 0.6 MAYO 80 1.8 61 1.6 MCGILL 12 0.3 15 0.4 MSKCC 121 2.8 97 2.5 MUV 123 2.8 77 2.0 NCI 22 0.5 61 1.6 NICCC 61 1.4 108 2.8 OCGN 65 1.5 112 2.9 OSU CCG 33 0.8 28 0.7 OUH 89 2.1 117 3.0 SMC 47 1.1 57 1.5 SWE-BRCA 23 0.5 31 0.8 UCHICAGO 25 0.6 12 0.3 UCLA 15 0.3 26 0.7 UCSF 16 0.4 11 0.3 UKGRFOCR 4 0.09 13 0.3 UPENN 134 3.1 105 2.7 WCP 17 0.4 54 1.4

TABLE 4 Quality control filtering steps for BRCA2 mutation carriers and SNPs on the COGs array Remaining Remaining No. of No. of No. of No. of Sample Data Cleaning Steps/Exclusion Reasons samples Samples Data Cleaning Steps for SNPs SNPs SNPs Total Eligible Samples on Manifest with Genotype Data 10,048 Total SNPs on COGs Array 211,155 Ineligible based on phenotypic data 211 9,837 Y chromosome SNPs 79 211,076 Self-reported non-CEU ethnicity 531 9,306 Call rate <95% 4,446 206,630 Incorrect gender based on genotype 34 9,272 HWE(stratified) P-value < 10⁻⁷ 1,845 204,785 Call rate <95% | Heterozygosity: P-value < 10⁻⁶ 300 8,972 Monomorphic markers 3,853 200,932 >19% inferred non-CEU ancestry 166 8,806 Unreliable SNP genotypes 1 200,931 Discordant with previous CIMBA genotyping 53 8,753 SNPs with high discordance rate among 23 200,908 Consistent duplicate pairs (one sample excluded 498 8,255 known duplicates (list obtained from all Inconsistent duplicate pairs (both samples excluded) 44 8,211 members of COGs consortia) Totals After Filtering 1,837 8,211 10,439 200,908

TABLE 5 Breast cancer hazards ratios (HR) and 95% confidence intervals (CI) for all SNPs with P < 10⁻³ in a 500 Mb region around rs9348512 on 6p24 among BRCA2 mutation carriers Minor Minor r² with Allele r² P- SNP Position Type¹ Major Allele Allele rs9348512 Freq. imputation value² rs9348512 10564692 typed C A 1.00 0.34 1.00 4.4 × 10⁻⁸ rs9358529 10563215 imputed A C 0.86 0.31 0.96 8.2 × 10⁻⁷ rs303067 10548212 imputed A T 0.71 0.40 0.95 1.9 × 10⁻⁶ rs1348 10557244 imputed T C 0.51 0.21 0.96 1.0 × 10⁻⁵ rs9366443 10565096 imputed C T 0.72 0.41 0.94 3.7 × 10⁻⁵ rs9460713 10555412 imputed C T 0.49 0.20 0.96 4.9 × 10⁻⁵ rs9466289 10555931 imputed T C 0.50 0.20 0.97 5.4 × 10⁻⁵ 6-10546956 10546956 imputed A AGG 0.48 0.41 0.84 5.9 × 10⁻⁵ rs9466290 10555941 imputed G A 0.49 0.20 0.96 6.4 × 10⁻⁵ rs3911709 10559876 imputed G A 0.45 0.26 0.96 7.3 × 10⁻⁵ 6-10557995 10557995 imputed GTAT G 0.49 0.20 0.95 7.4 × 10⁻⁵ rs9295542 10565669 imputed A G 0.61 0.46 0.91 8.2 × 10⁻⁵ rs6908107 10559449 imputed C G 0.61 0.46 0.98 8.8 × 10⁻⁵ rs35076407 10563463 imputed T C 0.62 0.45 0.98 9.2 × 10⁻⁵ rs602199 10554912 imputed C G 0.60 0.39 0.94 9.3 × 10⁻⁵ rs7738545 10563318 imputed C T 0.62 0.45 0.99 9.7 × 10⁻⁵ rs303074 10560093 imputed G A 0.61 0.46 0.98 1.0 × 10⁻⁴ rs78113724 10543366 imputed G A 0.45 0.24 0.97 1.1 × 10⁻⁴ rs303073 10560449 imputed A G 0.61 0.46 0.98 1.3 × 10⁻⁴ rs4712668 10562060 typed G T 0.61 0.45 1.00 1.4 × 10⁻⁴ rs75769093 10551514 imputed C A 0.54 0.47 0.90 1.5 × 10⁻⁴ rs303070 10551187 imputed G T 0.56 0.48 0.92 1.9 × 10⁻⁴ rs9393239 10550632 imputed C T 0.43 0.21 0.98 2.0 × 10⁻⁴ rs303061 10538157 typed T C 0.41 0.24 1.00 2.1 × 10⁻⁴ rs4097280 10561081 imputed G A 0.58 0.44 0.95 2.3 × 10⁻⁴ rs4710998 10541811 typed A G 0.39 0.22 1.00 2.8 × 10⁻⁴ rs6907578 10532198 imputed T A 0.53 0.43 0.86 3.7 × 10⁻⁴ 6-10550552 10550552 imputed G T 0.55 0.47 0.90 3.9 × 10⁻⁴ rs56365413 10540162 imputed C T 0.40 0.20 0.98 4.0 × 10⁻⁴ 6-10545251 10545251 imputed GTTGTTGTT G 0.38 0.21 0.99 4.4 × 10⁻⁴ rs303068 10549297 imputed A C 0.56 0.48 0.92 4.5 × 10⁻⁴ rs6923826 10532295 imputed C G 0.50 0.42 0.86 4.8 × 10⁻⁴ rs12175352 10545445 imputed T C 0.38 0.21 0.99 4.8 × 10⁻⁴ rs303065 10541072 imputed C T 0.39 0.20 0.99 5.0 × 10⁻⁴ rs303064 10540524 typed C T 0.38 0.20 1.00 5.2 × 10⁻⁴ rs9295535 10547954 typed T C 0.40 0.21 1.00 6.2 × 10⁻⁴ rs115262601 10526048 imputed A C 0.01 0.01 0.44 6.6 × 10⁻⁴ rs6924202 10532454 imputed C T 0.56 0.39 0.80 6.7 × 10⁻⁴ rs12526269 10536199 imputed T A 0.37 0.23 0.98 7.1 × 10⁻⁴ 6-10546510 10546510 imputed GC G 0.48 0.44 0.94 7.9 × 10⁻⁴ rs303063 10538964 imputed C T 0.38 0.20 0.99 8.7 × 10⁻⁴ ¹Type indicates whether the SNP was genotyped or imputed. ²p-value was calculated based on the 1-degree of freedom score test

TABLE 6 Associations with SNPs at 6p24, FGF13 and 2p22 and breast and ovarian cancer risk using a competing risk analysis model SNP rs No. No. breast No. ovarian Ovarian cancer Breast cancer Chr. No. unaffected cancer cancer HR (95% CI) p-value HR (95% CI) p-value rs9348512 3432 4310 468 0.98 (0.84, 1.13) 0.74 0.84 (0.79, 0.90) 8.7 × 10⁻⁸ 6p24 rs619373 3432 4307 468 0.98 (0.73, 1.30) 0.88 1.29 (1.15, 1.44) 7.1 × 10⁻⁶ Xq26 rs184577 3432 4311 468 1.01 (0.86, 1.19) 0.89 0.85 (0.79, 0.92) 1.4 × 10⁻⁵ 2p22

TABLE 7 >gnl|dbSNP|rs1348 rs = 1348|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/G”|build = 137|suspect = ?|GMAF = C: 2184:0.1603 GAAAGTATTG ATGGTGAGTA ACTTTGGTAA AAGAAGGTCC AGGCATGTGG CTCATGCCTA TAATCCCAGC ACTTTGGGAG GCCGAGGCAG GATAATTGCT TGAGGTCAGG AGTTCGAGAC TAGCCTAGGT AACATAGTAA AACCCTGCCC CTACAAAAAA TTAAAATATT AGCCAGGTGT GATGGTGCAC ATAGTCCTGT CATCCTAGCT ACTTGGGAGG CTGAGGCAGG AGGATTGCTT GAGCCCAGGA ATTTGAGGCT ACAGTGAGCT ATGATTGTGT CACTGCACTC CAGCCTAGGC AACCGAGTGA GACTCCATTT CTAAAAGAAA TTAATTAATT AATAAAAATG AAGAGAATGC TCAGCAGACT TCTGAATTCT TTGAGTGTTC ACAAGAAAAA TAAATTCATG TATCACATGT CCTATAAAAG AAAAAGGGGT TGGGGTTGCT TTTGCATATA AAGCAAGCTA GTTTCAACCG AACTCTCTGT CCACTGGAGA R AAGTGCTTGC CACAGAAAAC TGTTTTTCTG GTTCACTGAG GTATAATTGA CAAATAACAA GTACATATAT CCAAGGTGCA CAATGTGATG TTTTGATATA AGTATACACT GTGAAAGGAT TATTACAACC AATTTGTAGT AATTAACTTG ATGAATTAAC ATATTCATCA AGTATATAGT ACATTATTAT TAACTGTAGT CACCATGCTG TGCTTTCCAT CTCAGAAAAC TTAGAGTATT ACCCTAAAGT TAACTATGAC CATTTAGAAG TTTGCTTTAA AACACTGCTT TTCAAATTTT ACTGTGGAAA CAAATCGCTT GGGGATCTTG TTAAAATTTG GATTCTGAAT CAGTAGGTCT AGAGTGGGGC CTGAGATTCT GTCTTTCTGA CAATCTCCCC AGGTGACACC AATGCTGCTG GTCCCTGAAG CACGCTTTTG GTGGCAAGGT TTCCAGAGAG CTGAGGCTCC TGTATTTCTT TACAATCCAG ACATCAGTTT >gnl|dbSNP|rs303061 rs = 303061|pos = 501|len = 1001|taxid = 9606|mol = “genomicm”|class = 1| alleles = “A/G”|build = 137|suspect = ?|GMAF = C: 2184:0.234 CACCTGCTAC TGATACACAC TATAAACGTG GAAGATGATT TTCATTTTTG TAGTCATGAG CAGGATACTG TATAATGTAT AATTGTTGGA CATTAAAGAA AACAAACTCC TTCTTGTCTC CTTAGGCTCA GAGCCACTCA GACATTGGGA AGCAAGTTTG TCAAGATGAC AGAGAACCGA GGTAATGGAT TCGAGTGATG AAACAGGAAG TTCATTCATG AGTTTTTGGC CACACCTCCA AAGTGACGAC TTAGCCAGAA ATGGGATAAC TGGGTTTCCC TACTTCTCTT TTATCATCCT CAATGAGAGT GACCAAATAT TAGAGCTAGA TGGAACCTTA GTGAAAATCT GGCTACTCGT CCCGTCCCAC CAGCCTGCCA CCCATTTCAA GTTTGAAGAG ACAAAGACAC ATGGACCTTA TGTAATTACT GGGGATTACC CCAGGAGTCT GTGGCAAAAG TCAGCTTCTT CCCTCCCTGC TTCCCCGCCC TGTCTCTGGT R CTTTCTACCA ACACTGGGCT GTTTCTGTGA TCACACTTAA GCGTACCTAA CCTGCGAATG CTGTATAGAA GGTGCTAATG AACATGATTT AGCTTTAACA CTCAGTTTTC TAAAGGGACA CGTGGGGGCA GCAAATGTTT AGGCAAAAAC AATTCCAGTT CTAGCCTCTA CTGTCTACAT ATGTGTATAC ATTTGGGAAA CGTTTGGGAA AGGGATATTT GAGAGCTTCT TTTTCTTTTT TGTGGTTTAG TTATTTGATG ATATTGAGAT TGTTTCTGAG CCATGTGCTT CAACATCGGA TTGGGGATTT CAGAAAAAGT TTTAGTCACT GTGATTCCAT TTAGCTTCCA AATGTGTCTC TGCTAAGAGA CTTAAAAGCA CTCATAAATA GCACGTGTGT CTTCTTTGCA GTGTTTGCTA ATTTTGAGTC ACATCTTTTT AGAAAATCAT GAGATTTGGT GTCACAGAGA CTGGAATAAA TATAGTCAAA CTTATTGGTG >gnl|dbSNP|rs303063 rs = 303063|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/G”|build = 137|suspect = ?|GMAF = T: 2184:0.2079 CACGTCTGTC ACTCTAAAGC CTGTTCTAAG ATCACATCCC CTCTGGCTCT CCCTAGATAC TTCTGATTAT AGTTGGTAAT CATGCCCATT AAATGTCTTT GGTTTCTTGG TACACAGAGA GAAGGTAAAT AATGGGTTCA TGATTTAGGT AAGAATACAC AAATATTTTT CACACTATAA ATCTGTTTTC TATTCAGTTA AATATTAGAC CATCTGGTAA TGGAGAAAAT GTTTGAAAAT CCACCTAGTT AACCTAAAAG TTTGCATTTC CTGCCTTTGG GGCTTTGAAT TTTTAAGTTA CATGTCTTCG TAGATGGTGA TCAGGATAAA ACTAATATCT CTCTTAGATG AATCAAAATT CACCCCCTTG GGCAAGGGAG GGACCCAGCC TTTCTTTAAA CATGTGGTTG CCTGAGAGTT GAACAAAACT GGAATAGGGA GAGAATGGCT TTGCCATGCT GATCATGGTA TTGTGGAGAT TTAAGAACTA GTGGCCAGGC R AGGTGGCTCA CGCCTGTAAT CCCAGCATTG TGAGAGGCCA AGGCAGGTGG ATCACTTGAG GCCAGGATTT CGAGACCAGC CCGGCCAACA TGATGACCCT GTTTTTACTA AAAATACAAA AATTAGCCGA GTGTGGTGGT GTGTGCCTGT AGTCCCAACT GAGGCATGAG ACTTGCTTCA ACCCAGGAGG CGGGGGTTGC AGTGAGCCGA GATTGTGCCA CTGCACTCCA GCCTGGGTGA CAGAGTGAGA CTCTGTCTCA AAAAAAAAAA AAAAAAAAAC TAGTCAGAGC TGTTGAGGTG TTGAGGCACC TGCTACTGAT ACACACTATA AACGTGGAAG ATGATTTTCA TTTTTGTAGT CATGAGCAGG ATACTGTATA ATGTATAATT GTTGGACATT AAAGAAAACA AACTCCTTCT TGTCTCCTTA GGCTCAGAGC CACTCAGACA TTGGGAAGCA AGTTTGTCAA GATGACAGAG AACCGAGGTA ATGGATTCGA >gnl|dbSNP|rs303064 rs = 303064|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/G”|build = 137|suspect = ?|GMAF = T: 2184:0.2092 ATAGTGAGAT TCTGCCTCTA AAACAAAACA AAATAAAAGA CAAAACAACT AATTCTGTTT TTTAAAAAAA AAAAACACAC AACAGCAAAC TTGACTATAA AGATTATTGC TGGGCACGGT GGCTCAGGCC TGTAATCCCA GCACTTTGGG AGGCTGAGGC GGGTGGATCA CCTGAGGTCA GGAGCTCAAG ACCAGCCTGG CCAACATGGT GAAACCCTGT CTGTACTAAT AATACAAAAA ATTAGCCGGG CATGGTGGTG CATGCCTGCA ATCCCAGCTA CTCGGGAGGA TGAGGCAGGA GAATCACTTG AACCTGGGAG GTAGAGGTTG CAGTGAGCCG AGACTGCGCC ACTGCACTCC AGCCTGGGCA ACAAGAGCAA AACTCCGTGT CCAAAAAAAA AAAAAAAAAA AAAGATTATT ATATATAATC ATTCAAGGCC TGTATGACTC AGTTCCCTTA GAAAAATGTC ATAATTTTTA TATTACTGAA TATTATTGGC R TTATTTGTGT AGCCCACTTA AGTGAAGTCA ATAACATGAT TAAGTGGCAT ATTATCTTCA TGTCAGTCAA ACGTTATTTG GATTTTATAA GTTAGGGTGA GATACAAATA AGTGAAAATA CTTTTTCTAA TGAATAATGA TGAATCTAAA ATAGGATTGA CTTGGCTGGG CATAGTGGCT CGTGCTTGTA ACCCCAACAC TTTGGGAGGC TGAGGCAGTA GGATTACTTG AAACCAGGAG TTTGAGACCA GCCTCGGCAA CAAAGGGAGA ACTCTTCTCT AATAAAAATA AGAATAAAAA ATTAGCCAGG TGTGGCAATG TTCACCTGTG GTCCCAGCTA CTTGGGAAGC TGAGGCAGGA GGATCGTTGG AGCACAGGAG TTCAAGACTG CAGTTAGCGG TGACTGCACT CCAGCCTGGG CAATAGAGCA AGACCCTGTC TCTAAAAAAA AAATAATAAT AAATAGGACT GGCTCGCATA TGTATGCAAC TATTTTGTTA >gnl|dbSNP|rs303065 rs = 303065|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class  = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = T: 2184:0.2083 ACTGAGTCAT ACAGGCCTTG AATGATTATA TATAATAATC TTTTTTTTTT TTTTTTTTTT TGGACACGGA GTTTTGCTCT TGTTGCCCAG GCTGGAGTGC AGTGGCGCAG TCTCGGCTCA CTGCAACCTC TACCTCCCAG GTTCAAGTGA TTCTCCTGCC TCATCCTCCC GAGTAGCTGG GATTGCAGGC ATGCACCACC ATGCCCGGCT AATTTTTTGT ATTATTAGTA CAGACAGGGT TTCACCATGT TGGCCAGGCT GGTCTTGAGC TCCTGACCTC AGGTGATCCA CCCGCCTCAG CCTCCCAAAG TGCTGGGATT ACAGGCCTGA GCCACCGTGC CCAGCAATAA TCTTTATAGT CAAGTTTGCT GTTGTGTGTT TTTTTTTTTT AAAAAACAGA ATTAGTTGTT TTGTCTTTTA TTTTGTTTTG TTTTAGAGGC AGAATCTCAC TATGTTGCCC AGGCTTATTT TGCACCACTG GCCTCAAGCA GTCCTCCCAG Y TCTGCCAAAT TTTAGACACC TGGACTTGGA AACCCATGAG AAGTTGGCCA GCGCTTCCTT TTGCATTTAT GCAGAGCAAT GGTAAACGTC AGCAGCAAAT TTACAATCAA TCTTATTTTC CAGTGTCTCC AGAGATTTGA TTGTTTTGCT TATATGGCTT ATGGCAATAC TTACCTCCAA TGTCTGATTT TTCAAAAAAA TTTCATCTAA TCCTTTGTGC AATGGTTTAC ATCTAATTTT TTTTGTTCTG AGATGTAGAC AGCTATTAAG AACTGATCTT TGCACAGTAA ATTTTTCCTA TTCTTAGTCA TTATCCTTAG GTGAGGCAAC ATGGTGCCAT TCAGTTATTC AGCAAATTCT CACATCTTCC GTGTGCCATG CATTGTTACA GGTTTTGAGC CTAGAACCAT GAAAACAAAA GACAAAAATC TCTGCCCTTG TGGACTTTGT ATTCCAGAGG AGAGAAAATA AACAAGGTAA GTAAAATATA TAGTATGGTA >gnl|dbSNP|rs303067 rs = 303067|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/T”|build = 137|suspect = ?|GMAF = A: 2184:0.4986 TGTGTTCATT ATGACCACTG GGAAGATGAG TTTCACCCTT CACATGACTA TATGGTGAGA ACATGTTTCT CAGCTGAGAC AAGATTTCAG GAAAGTTTGC AAGAACCGAG AGAAAATGGA AGAAAATGAA ATATTTGTTC TTCAGAGTCA CCAGTTTTAT TATATGCCTG GACTCTGTCA CTTATGTCAA TAAATTTACA AATGCAAAAT ACACATTTAA TTCCAGCGTG GTAGCATACA CCTGTAGTCC TAGATACTCA GGAGGGTGAG TATCTAGGAC TACAGGTGTG TGCTACCACG CTTGAACTCA GGAGTTCAAG GCCAGCCTGG ACAACACAGG GAGACCCCCT CTCTAAATGT ATATACACAC ATACACACAC ACACACACAC ACACACACAT TCAAACATTG GAATCAGATG TCCTGGACAA AATGCTCAAA TCAGCTGAAC ACTTTGGAAT GCTTAACTTT TCTTTTTTTT AGATGTTTAT TTATTTATTT W TTTTGTTTTT TATTTTATTA TTATTATACT TTAAGTTTTA GGGTACATGT GCGCAATGTG CAGGTTTGTT ACATATGTAT ACATGTGCCA TGTTGGTGTG CTGCACCCAT TAACTCGTCA TTTAGCATTA GGTATATCTC CAAATGCTAT CCCTCCCCCC TCCCCCCACC CCACAACAGT CCCCGGAGTG TGATGTTCCC CTTCCTGTGT CCATGTGTTC TCATTGTTCA ATTCCCACCT ATGAGTGAGA ACATGCGGTG TTTGGTTTTT TGTCCTTGTG ATAGTTTGCT GAGGATGATG GTTTCCAGTT TCATCCATGT CCCTACAAAG GACATGAACT CATCATTTTT TATGGCTGCA TAGTATTCCA TGGTGTATAT GTGCCACATT TTCTTAATCC AGTCTATCAT TGTTGGACAT TTGGGTTGGT TCCAAGTCTT TGCTATTGTG AATAGTGCCG CAATAAACCT ACGTGTGCAT GTGTCTTTAT AGCAGCATGA >gnl|dbSNP|rs303068 rs = 303068|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/C”|build = 137|suspect = ?|GMAF = A: 2184:0.4148 CACTGACTTC CACAATGGTT GAACTAGTTT ACGGTCCCAC CAACAGTGTA AAAGTGTTCC TATTTCTCCA CATCCTCTCC AGCACCTGTT GTTTCCTGAC TTTTTAATGA TCACCATTCT AACTGGTGTG AGATGGTATC TCATTGTGGT TTTGATTTGC ATTTCTCTGA TGGCCAGTGA TGATGAGCAT TTTTTCATGT GTTTTTTGGC TGCATAAATG TCTTCTTTTG AGAAGTGTCT GTTCATATCC TTCGCCCACT TTTTGATGGG GTTGTTTATT TTTTTCTTGT AAATTTGTTT GAGTTCATTG TAGATTCTGG ATATTAGCCC TTTGTCAGAT GAGTAGGTTG CAAAAATTTT CTCCCATTTC GTAGGTTGCT TGTTCACTCT GATGGTAGTT TCTTTTGCTG GAATGCTTAA CTTTTCTTTC CTGCAAGAGG AAGACTGGGA GATTGAGAGG TGTGCCCAGG GTGTCAGCTC AGTGCCTGGT AGAGGCAGGT M ACATGAGCTT TGAGCCCTGG CCTGTGGATT GTGTTGTGCT TGGACTGGCC TGTTACGCCA TCTGCTTGCT TCTGCCTGAA ATTCCTGTAA CACAAGATAA AACCTCTCAT TCTGCAATTT ACCAATAAAC CTTATAAAAT CTGAGGCTAA GCCTTGTGAA ATGGCAAACC CTAGAAAGCC AGAGAGCTGG CATGCGGTAG TTCAGCAGTG GCTGCAGGAA ATAGAAGGAA AGGGAGTGAG GAGACAGGGT GCAAGCTGGA ATGTGATCAC AGGGGAGGGG GATCTCTGGA CTATGGGGGA ATGACGGGCA GCTTGAGGCC CTGAGATGAA AGACACTCCC TTTAAGAAAA ACGTCTGTGA CTCAGAGCCA CAAGGCGTGT CAGGAGAAAG TTCTGATGAA ACTGCATTCC ATTCCTGGAG GAATACGCAT TGCCATTGAA GTACAAACTC TAAAATGATG TAGGATTAAT AATTAAGGCA ACACTAGTAG TGGATGGTGG >gnl|dbSNP|rs303070 rs = 303070|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “G/T”|build = 137|suspect = ?|GMAF = G: 2184:0.4038 TTCTTTTAAA ACTAAACATA TACAGTTGTC CATCAGTATC TGCAGGGGAC TGGTTCCAGG TCTCCCTGCA AGTACCAAAA TCACAGATGC TCAGGAATCT GATATAAAGC AGGGTAGTAC TTGCATATAA CCCACACACA TCCTCCTGTA TACTTTAAAT CATCTCTAGA TTACTTATAA TACCTAGTAC AAGGTGAAGA CTATGTAAAC AGTTGTTATA CTGTATTTAA AAAAATAATT TCCACTTTCA TTTTAGATTC AGGTGGTACA TATGCAGGTT TGTTATATAA GTGTATTGCA TGATGCTGAG GTTTGGAGTT CAATTGATCC CATCACTCAG ATAGTGAGCA TAATTATACT GTATACAGTT TAGGAAATAA TAACAAGAAA AAAGTCTATA CATATTCCAT ACAGACATGA TCATCCTTTC CTCCCACCCC TTGATTTTTT ATTGAATTCA TGAATATTTG CTGAGTCCAT GAATGTGGAA CCCACGGATA K GGAAGGCTGA CTGCACCTAC ATTATGACCT GGCAATTCCA CACCTAGGTT ACTCACCCGG GAGAAATAAA AGCATATGTC CTCAAAGAGG CTTGTTCAAA AATGTCCATA GCTTTATTCA TAAATAACTG AAAGCTGAAA ACAACCAATA GGAGAATGAA TAAACTAACT GTGGTAAATT CAGACAATGA AATACTACAC AATAAAAAAG GGAGGAACCG GCTGGGCGCG GTGGCTCACA CCTGTAATCC CAGCACATTG GGAGGCCGAG GTGGGTGGAT CACCTGGGGT CAAGAGTTCG AGACCAGCCT GGCCAACATG GTGAAACCCC ATCTCTACTA AAAATACAAA AATAGTCAGG TGTGGTGGCA CGCACCTCCA ATCCCAGCTA CTCGAAAGGC TGAGGCAGGA GAATCAGCTT GAATCCAGGA GGCAGAGGTT GCAGTGAGCT GAGATTGTGC CACTGCACTC CAGCCTGGGT GACTCTGTCT CAAAAAAAAA >gnl|dbSNP|rs303073 rs = 303073|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = A: 2184:0.4139 ATGAACTAGG TTTTGAACTC CCCCATGCAT GACCCCTGCC TGTGGGGTCT GCCCTAGATG GATAATAAAT AGGTCATTAT CAGTCTCATT TCAGTGTCCA CAGTGGAGAG ATTTTATTCT TCCCCTTGCT CTGGAACTGG CCCCTTTTCT CCTCCAAATC CCAATCTTGG CCTAGAATTT TGAACTCTGC TTAGAATTCC AAATTGCCAC ATATATATGT CAGGAGATCA GACAGAGTTA GCTGAGCAGG GAACAAGGCC GTGCTTTTCA GAAGTATGTA GGTCTGCTTC ACAAGAATAT GCCATTAACA ATATGGACAA GGCTCACCAT AAATTTATGA GTGAAACAAC TTATTCCAAC TGCTCTCATG CCTGGCTTTA TACAGTCATC TACTTGTCCT TCCCTGGGCC CAGCCAATGC TGCTCCCCCT TTAACAACTG CTTCTGAATG TCCCTGTGGT GTGGGCCAGA AAGGAGACTC TCTTCTTCCC CAAATCCACC Y GCAGTATGGC AGAAACTAGA ATTCATGGTC CTCTCCCAAC CCATGCCCAC TTCCTTCTGC CACTTAAAGA AAACACCCAT AAAGGGTGGG AAGAGAGAGC GTAACAGCAA GGTCTGTGCA TTCCCAGAGA TGTGATGCAA GGGGTGTGGG AGGCATGGCA CTGCTTGACT CACGCTGGAG AGCGGGCACT TGGCCTGGCT TTCAGAGGAA ATGCTCCTTG GAATGCGGTC GGCCCCGGCT GCACCCACGC CTGTGAGGGA GGGGCTTATG TGTCTGGCAC ATCATAGGTG GCTCCTGGGG TTTGCCATGA GTCTCAGCAC AGCAGACCTG AGAGGAAAAA AATACAGACT GAACGCGTTT CTTCTATTCT CTCACCCAAC ACAGAAGACT TCTGTGGCCG CATGTGTGGG GTTTTTTTTC CCCACATACC AAGAAGCAAA CAATTCTTCT GTGGACTCTA GCCGAGACTC TTCCAATTCA ATTCAATTCA GACACTAGCT >gnl|dbSNP|rs303074 rs = 303074|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = G: 2184:0.4245 CAACTGCTCT CATGCCTGGC TTTATACAGT CATCTACTTG TCCTTCCCTG GGCCCAGCCA ATGCTGCTCC CCCTTTAACA ACTGCTTCTG AATGTCCCTG TGGTGTGGGC CAGAAAGGAG ACTCTCTTCT TCCCCAAATC CACCTGCAGT ATGGCAGAAA CTAGAATTCA TGGTCCTCTC CCAACCCATG CCCACTTCCT TCTGCCACTT AAAGAAAACA CCCATAAAGG GTGGGAAGAG AGAGCGTAAC AGCAAGGTCT GTGCATTCCC AGAGATGTGA TGCAAGGGGT GTGGGAGGCA TGGCACTGCT TGACTCACGC TGGAGAGCGG GCACTTGGCC TGGCTTTCAG AGGAAATGCT CCTTGGAATG CGGTCGGCCC CGGCTGCACC CACGCCTGTG AGGGAGGGGC TTATGTGTCT GGCACATCAT AGGTGGCTCC TGGGGTTTGC CATGAGTCTC AGCACAGCAG ACCTGAGAGG AAAAAAATAC AGACTGAACG Y GTTTCTTCTA TTCTCTCACC CAACACAGAA GACTTCTGTG GCCGCATGTG TGGGGTTTTT TTTCCCCACA TACCAAGAAG CAAACAATTC TTCTGTGGAC TCTAGCCGAG ACTCTTCCAA TTCAATTCAA TTCAGACACT AGCTACCTAG AGATAGTGTA AGAAGGCACA GGTTGAGGAC TCATTCCCCA AGACCACCCC TCACTCCTGA TGCCAACTGC AAGCTCCACG TTGTTTTATC TGTGCATCTG ACTGGCTATA AATCAGGGGT CCTATGGCCC CCTCCGGTGG CCCTTCCTTG GGTTTGATTA ATATGCTAGA GCAGCTCACA GAACTCAGGG AAACACCGAT TTATTATAAA GGATATTACA AAGGATAAAG ATTAATAGAT GCATAGGGTA AAGTATGGGG GAAGGCAGAA GCAGTTTCCA CGTCCTTCCC AAGCACCACA CCCTCCAGGA ACCTCCACGT GGTCAGCTAC CCAGAAGGTC TCCAAACATG >gnl|dbSNP|rs602199 rs = 602199|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/G”|build = 137|suspect = ?|GMAF = G: 2184:0.4505 GCAGATGGGG CCTTTGGGAG GTGACTAGGA CATGAGGGTG GAGCACTTGT GAGTGGGATT AGTGCCCTTA CAGAGAGACT GCAGAGAGCT CCCTTGCCCC TTCCACCATG TGAGGACACA GGGAGAAGAT GGCTGTCTGT GAACTGGGAA ACAGGCCCTC ATCAGACACT GAGTCAGCTG AGGTATTGAT CTTGGACTTC CCAGCCTTCA GAACTGTGAG AAATAAATTT CTGTTTAAAA GACACCCAGT TTCAGGTATT TTTGCCATAG TAGCCCAAAC AGACTAACAG TGGATATAAT TTTGTTGAGC CAGTTATAAA ATCCAAGTCC AAGTCAAAAC TGCAGGCTGA TATTAGCTGG GCATGGTGGT GCATGCCTGT AGTTCCAGCT ACTCCAGAGG CGGAGGTGGG AGGATGGCTT GGGCCCCAGA GGTCAAGGCT GCTGTGAGTG GTGATCACAC CAATGCACAC CAGCCTGGGG GACAGAGCAA GACCCTGTCT S AAAAACACAA AAATACAAAA AACTTGCAGG CTCCAAAACT ACCTGAACTT CAACCTACCT TCTTTCAATA CAATGCCCAG AACATAGTCT CTCTCAATGA CCCAGTCAAG CTTAAAAAAA AAAAAAAGAA AAAAAAAGCA TCACCACTTC TCTCTTCAAA ATCTAGCCTT GATATATATT TTGAGGGGAT GCATTCTGAA GTGTGTCAGC ATGATACATC GTATCATCTG CAACTTACTT TCAAATGGCT TAGGAAAAAA ATATGGTATC TGCTGTCTAT TTATCTACCT ACCTATCTAT CAAGAGATAA GCAAACATGA TGAAATAATA ACAGTTGTTA AATCCATGTG AGGGGTATTT GGCTGTTTGT GTGCTATTTT TCAGCTTTTC TGATTGAGCT TACTTTTTTA AAAAGGCAGG AAAAAGTATG TGGCCTTGAC TGTGAAGACA GACAAGAAAC AGCTGAGCCC CTCTTGTTTC TCGATACATC AAAAATGCGG >gnl|dbSNP|rs3911709 rs = 3911709|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = A: 2184:0.3173 ACACCCATAA AGGGTGGGAA GAGAGAGCGT AACAGCAAGG TCTGTGCATT CCCAGAGATG TGATGCAAGG GGTGTGGGAG GCATGGCACT GCTTGACTCA CGCTGGAGAG CGGGCACTTG GCCTGGCTTT CAGAGGAAAT GCTCCTTGGA ATGCGGTCGG CCCCGGCTGC ACCCACGCCT GTGAGGGAGG GGCTTATGTG TCTGGCACAT CATAGGTGGC TCCTGGGGTT TGCCATGAGT CTCAGCACAG CAGACCTGAG AGGAAAAAAA TACAGACTGA ACGCGTTTCT TCTATTCTCT CACCCAACAC AGAAGACTTC TGTGGCCGCA TGTGTGGGGT TTTTTTTCCC CACATACCAA GAAGCAAACA ATTCTTCTGT GGACTCTAGC CGAGACTCTT CCAATTCAAT TCAATTCAGA CACTAGCTAC CTAGAGATAG TGTAAGAAGG CACAGGTTGA GGACTCATTC CCCAAGACCA CCCCTCACTC CTGATGCCAA Y TGCAAGCTCC ACGTTGTTTT ATCTGTGCAT CTGACTGGCT ATAAATCAGG GGTCCTATGG CCCCCTCCGG TGGCCCTTCC TTGGGTTTGA TTAATATGCT AGAGCAGCTC ACAGAACTCA GGGAAACACC GATTTATTAT AAAGGATATT ACAAAGGATA AAGATTAATA GATGCATAGG GTAAAGTATG GGGGAAGGCA GAAGCAGTTT CCACGTCCTT CCCAAGCACC ACACCCTCCA GGAACCTCCA CGTGGTCAGC TACCCAGAAG GTCTCCAAAC ATGGTTCTTT TGGGCTTTGA TGGAGGCTTT ATTATGTAGG CATGATTGAT TAAACTCTTG GTCATTGGTG ATTAACTTTA CTTCAGCCCC TCTTCCCTTC CCAGAGGTTG GGGGATGAGG CTGAAAGCCC CAACCCTCTA ACCATGGCTT AGCCTTTCCC ATGACCAGTC TCCGTTCTGA AGCTACCGAT GGGCTGCCGG CCATCTGTCA ACTCATCAGC >gnl|dbSNP|rs4097280 rs = 4097280|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = A: 2184:0.4789 GCTGGTCTCA AATTCCTGAC CTGAAATGAT CCGCCCGCCT CGGCCTCCCA AAGTGCTGGG ATTACAGGTG TAAGCCACCA CGCCTGGCCT GCCCATCTCT TTTTCACTTG AGCCAAAGCT ATTTCTAGGA AGGCAGTGGC ATTTCCTGAG CTAAAATCAT TTTCCCATTC CTGAGTCACA TTTCACATGG TCCCAGAGGT GAATTTAGTG GATTACATTT TAAAAAACAA ACAAAAACCT CAGAGCCACA CATAGACCAG GTTTTGCCTT CTTCCTCTCC AACTCCCACT ATTCCTTCCT TTGCACGTTT GCTGAGCCAT ACGGAAGTGC ATGGCCAACA GAGAAGAAAA AGGTTGATTA GTAGTAAAGA AGCCCTGCTG TTGCCTTGAA TGTCAGCACG TGCACACACA CACACAGGTG CGCGCACACA CGGGCACACA CAGGTACGCG CACACACACA GGTGCAAGCA CACAGGTGCG CGCACACACA CAGATGCGCG Y GCACACACAG GTACGCGCAC ACACACTTTT ATACCTGTCC ATTGCCAGTT TCTTTTGGTT CTTCAGGATT GCCCTTAGTG TTCGTTTTCA TCTGATGTTG CCTAGACCAA AATCCTGGAG GAAAGAACCT GATGAACTAG GTTTTGAACT CCCCCATGCA TGACCCCTGC CTGTGGGGTC TGCCCTAGAT GGATAATAAA TAGGTCATTA TCAGTCTCAT TTCAGTGTCC ACAGTGGAGA GATTTTATTC TTCCCCTTGC TCTGGAACTG GCCCCTTTTC TCCTCCAAAT CCCAATCTTG GCCTAGAATT TTGAACTCTG CTTAGAATTC CAAATTGCCA CATATATATG TCAGGAGATC AGACAGAGTT AGCTGAGCAG GGAACAAGGC CGTGCTTTTC AGAAGTATGT AGGTCTGCTT CACAAGAATA TGCCATTAAC AATATGGACA AGGCTCACCA TAAATTTATG AGTGAAACAA CTTATTCCAA CTGCTCTCAT >gnl|dbSNP|rs4710998 rs = 4710998|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/G”|build = 137|suspect = ?|GMAF = G: 2184:0.2056 TTTTTTGTTC TGAGATGTAG ACAGCTATTA AGAACTGATC TTTGCACAGT AAATTTTTCC TATTCTTAGT CATTATCCTT AGGTGAGGCA ACATGGTGCC ATTCAGTTAT TCAGCAAATT CTCACATCTT CCGTGTGCCA TGCATTGTTA CAGGTTTTGA GCCTAGAACC ATGAAAACAA AAGACAAAAA TCTCTGCCCT TGTGGACTTT GTATTCCAGA GGAGAGAAAA TAAAcAAGGT AAGTAAAATA TATAGTATGG TAGATAATGA ATGGTATGGG CTGAAGGAAA AACATAAACA AAGACGTTAG GTAGTGCTGA GAGGGAGGGT GGTTTGCAGT TGAGATAAGG ATGTCTCCAA GGATATGGTG GCATCTGCAT TACCAGAGAA ACATTCCAGG CAGAGAAACC AGTGAGTGCA AAGGCCCTGA GGCAGAAGCA TGGCTGGCTT GTGTGGTAGG CATGAGTGAG CTAAGGACAG AGGAGCTACA GAGGACGCCA R AGAGAAATTG AGAATTGAAG GGAGTTGGGG GATGGAGCTG TTGGTACCGA AGGGAGACAT TATAAAGACC TTAGCTTGGC TGGGCACTGT GGCTCATGCC TGTAATCCCA GCACATTGGG AAGCCGAGGC AGGTGGATCA CCTGAGGTCA AGAGTTCGAG ACCAGCCTGG CCAACATGGG GAAACCCCAT CTCTACTAAA AATACAAAAA TTAGCCGGGC GTGGTGGCGT GCACCTGTAA TCCCAGCTAC TTGGGAGGCT GAGGCAAGAG AATCGCTTGA ACCCGGGAAG TGGAGGTTGC AGTGAGCCAA GATCACACCA CTGCACTGCA GTCTGGGCAA CAAGAGTGAA ACTCCATCTC AAAAAAAAAA AAGACCTTAG CTTTTCCTCT GAGAGCAGAA ACTTTGGAGG GGCTTGAGTC AAGCAATGGC GTGATCTGCT TCGTATCTTA ACAGAGTCAC CTTGGCTGCT CGGTCAGGGC AGGGGGACAA GTGTGGAGGC >gnl|dbSNP|rs4712668 rs = 4712668|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “G/T”|build = 137|suspect = ?|GMAF = T: 2184:0.494 AGGTCAGGAA TTTGAGACCA GCCTGGCTGA CATGGTGAAA CCCGGTCGCT ACTAAAAATT CCAAAAATTA GCCGGGCATC GTGGCGGGCA CCTGTAGTCC CAGCTACTCA GGAGGCTGAG TCTGGAGAAT GGCTTGAACC CAGAAGATGG AGGCTGCAGT GAGCCAAGAT CGTGCCACTG CACTCCAGCC TGGGCAACAG AGCGAGACTC CATCTTAAAA AAAAAAGAAA AGAAAGAAAA AGAAATGGGC AAGACAAAAC CAAGTTTAAG AATGCAAGTT TATTACTTAT GAAATTATAG ATTGCAGGAA CCAGAGACTT AAGTTTCTCC AGGCAGTAGT GTATATTATC AGGATGAGGT AAGGAACACC AGACCAACAG GGCAGACAGG TCTGATGAAG GAAAGGGACC TGAAGGTCAT TCTGAATCCA GTGGAGCTCT AAGTAGGTCA ACTTTTGGCC TCCTCAGACC AACATCCCTT TGTGGTGACT CAAGACCAAT K TCTACCTCAG GGTCAGGCTG GTTTGGTCAA CCACCTCCAG TATGGCTGAC TTAGTTTTCA AATTCAGCCA CAAGGATCAC ATTGAGGAGT CTTTTTTTCA GAGACAGGGT CTTGCCCTGT TGCCCAGGCT GGAGTGCAAT GGTACAATCA TAACTCACTG CAGCCTCGAC CTCTTGGGCT CAAGCAATCC TCCTGCCTCA GCCTCCTGAG TAGCTGGGAC TACAGACACA CACCACCATG CCCAGCTATT TATTTTATTT TTGTAGAAAT GGGTCTTGCT ATGTTGCCCA GGCTGGTCTT CAACTCCTGA TCCTCCCATC TTGGCCTCCC AAAGTGCTGG GATTACAGGC ATGAGCCACC TTGCCCAGCT GATCTTGAGG AGTAGTTCTT TCTTTGCTCT GAAGCCCCAT GGCTTCATTT GAATGTATGA CTAAGCCCCT GTTAATGGTA ACCAGTGAAA TGAATTATTA TTTTTTTTAT CATTTTTATT TTTTTTTGAG >gnl|dbSNP|rs6907578 rs = 6907578|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/T”|build = 137|suspect = ?|GMAF = T: 2184:0.4373 TTGCACTTCT GTTTACAAAG CAATCCTGCC ATCAAAAGAG GAACAAAATC ACCACTTATC ACACCCTGTC ATAAAGTAAT CTGCCCTGGA GGGAAACCCT TGTGGAGAAC TGCTGTGTGT GTGTGTGTGT GTGTGTGTGT GTGTGTGTGT GTGTGTTGGG GGAGGGGGGA TGGAGGAAAG GGGGATTCTC CCAACTCTTC TGCAGAGTAA ATCGCTGGAA CGCGTGGTGT CCCAACCGGC CTGGAAAGAC CGAGAGTACC ATGAGCTGTG AAGCTGGGGT GTGACAGGGA TGCCCGTCCA GGGCTGGCAA GAGTGCAGAA TGGCTCTCTT GGATCTTTGG AATAGGCACA TCTGCAGACC CCGCTCCAAT GTTTACTTTC CTAGCGCCTT CGAAGATACT CCCAAGGGCC CCCAAAATAG ATCAGCAAAA AGTGTTGGGG GTGGGGGGAG TGAAAAAGCC AGTTCTTGAA GACTGTAAGG TCCCCTTTCG CATCTCAGCA W CTGGAGTGTG CAGGGAATTC CTGACCAGTG GTTTTGCTCC CTCCAATCCC TTGCCTCCCC CCTCCCATGT TATGCACTTG TTCTTGGAGA GATGGACGTT AAAGAAGCGT CAAGCAGTTC TCACTGCAAA TAAATGGTGC AGAAATAAGA GAGAGAGGAT GAAAGCCTAG GAAGTTATAA GTGATCCTGA CCCGACCCAG CCACCAGGGG GTTATCTCTT TCCAGGTCCT GCCTTGTGTA GAGTGAGGTG ATAAACGCTT TAGGCAGCCA AATCCAAGCA CAGCTGGGTG CCTGGCGGGG ATGGGGTGGG GGTGGTCCTA TGTGGTGCCT CTGCCTCTGG AGTTACCTTT AGGAAAGGTC AAGAGAACTA TCCTCCCCTC CATGTCTGCT GAAAAGGGGC TATTTTGCTA GTCTTGTTAT CAGTAATTCA CCACTTAATA TAACCAGGTT TTAGGTTTGT ATATGAGCGA TCCTGGACAT CCAATACCAT CCCCCCAGTT >gnl|dbSNP|rs6908107 rs = 6908107|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/G”|build = 137|suspect = ?|GMAF = C: 2184:0.4277 TGTCCTACTG CCCTTCCCAG CACCTGGCTT TAATTGGGGC TGCTCTGGGC CACTTTGCCA GGAATCCTGA AGTTGATTTG TAGGGAACAG GGATTGAGTG ACCGGGCCCT ACCTCCGCTC CCCAAAAACA ATGTCCTGTT CTCATGTGCT GGCCCACCTC CTCCCCAGGA CCTGGGTCCC TACGCTGAAC ACTGAGGTGG CTTTTGCTCA GCTAGTCTCC AAGACAGCAC GAGCCTATTT TGCCTATATT GGTAAGAGTA ATGGAGCTGT TCATTCCAGT TATCTTTCAC TGGACTGAAA GGATTGGCTT AAAAAATTAC TGTACCCTAC TGCGATATTG AAAAATATAT ATTTCATCTT CCACCTTGTT TCCCTGTGTA CAACTCCTAA ATTCCTTGGA ATCTCCAAAG TGATGTCTTT TTTGTGTGCT GATGAGTTGA CAGATGGCCG GCAGCCCATC GGTAGCTTCA GAACGGAGAC TGGTCATGGG AAAGGCTAAG S CATGGTTAGA GGGTTGGGGC TTTCAGCCTC ATCCCCCAAC CTCTGGGAAG GGAAGAGGGG CTGAAGTAAA GTTAATCACC AATGACCAAG AGTTTAATCA ATCATGCCTA CATAATAAAG CCTCCATCAA AGCCCAAAAG AACCATGTTT GGAGACCTTC TGGGTAGCTG ACCACGTGGA GGTTCCTGGA GGGTGTGGTG CTTGGGAAGG ACGTGGAAAC TGCTTCTGCC TTCCCCCATA CTTTACCCTA TGCATCTATT AATCTTTATC CTTTGTAATA TCCTTTATAA TAAATCGGTG TTTCCCTGAG TTCTGTGAGC TGCTCTAGCA TATTAATCAA ACCCAAGGAA GGGCCACCGG AGGGGGCCAT AGGACCCCTG ATTTATAGCC AGTCAGATGC ACAGATAAAA CAACGTGGAG CTTGCAGTTG GCATCAGGAG TGAGGGGTGG TCTTGGGGAA TGAGTCCTCA ACCTGTGCCT TCTTACACTA TCTCTAGGTA >gnl|dbSNP|rs6923826 rs = 6923826|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/G”|build = 137|suspect = ?|GMAF = C: 2184:0.4455 CCTTGTGGAG AACTGCTGTG TGTGTGTGTG TGTGTGTGTG TGTGTGTGTG TGTGTGTGTT GGGGGAGGGG GGATGGAGGA AAGGGGGATT CTCCCAACTC TTCTGCAGAG TAAATCGCTG GAACGCGTGG TGTCCCAACC GGCCTGGAAA GACCGAGAGT ACCATGAGCT GTGAAGCTGG GGTGTGACAG GGATGCCCGT CCAGGGCTGG CAAGAGTGCA GAATGGCTCT CTTGGATCTT TGGAATAGGC ACATCTGCAG ACCCCGCTCC AATGTTTACT TTCCTAGCGC CTTCGAAGAT ACTCCCAAGG GCCCCCAAAA TAGATCAGCA AAAAGTGTTG GGGGTGGGGG GAGTGAAAAA GCCAGTTCTT GAAGACTGTA AGGTCCCCTT TCGCATCTCA GCATCTGGAG TGTGCAGGGA ATTCCTGACC AGTGGTTTTG CTCCCTCCAA TCCCTTGCCT CCCCCCTCCC ATGTTATGCA CTTGTTCTTG GAGAGATGGA S GTTAAAGAAG CGTCAAGCAG TTCTCACTGC AAATAAATGG TGCAGAAATA AGAGAGAGAG GATGAAAGCC TAGGAAGTTA TAAGTGATCC TGACCCGACC CAGCCACCAG GGGGTTATCT CTTTCCAGGT CCTGCCTTGT GTAGAGTGAG GTGATAAACG CTTTAGGCAG CCAAATCCAA GCACAGCTGG GTGCCTGGCG GGGATGGGGT GGGGGTGGTC CTATGTGGTG CCTCTGCCTC TGGAGTTACC TTTAGGAAAG GTCAAGAGAA CTATCCTCCC CTCCATGTCT GCTGAAAAGG GGCTATTTTG CTAGTCTTGT TATCAGTAAT TCACCACTTA ATATAACCAG GTTTTAGGTT TGTATATGAG CGATCCTGGA CATCCAATAC CATCCCCCCA GTTCCCCAGC GCCACTCCTG GACATTCTAG ACACCAGCGA GGCTTCTTCT GCAGCCATTC CTAATGTAGC AGATCCATTT TGGGGGGAGT CTGGATGCAG >gnl|dbSNP|rs6924202 rs = 6924202|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = T: 2184:0.4771 TACCATGAGC TGTGAAGCTG GGGTGTGACA GGGATGCCCG TCCAGGGCTG GCAAGAGTGC AGAATGGCTC TCTTGGATCT TTGGAATAGG CACATCTGCA GACCCCGCTC CAATGTTTAC TTTCCTAGCG CCTTCGAAGA TACTCCCAAG GGCCCCCAAA ATAGATCAGC AAAAAGTGTT GGGGGTGGGG GGAGTGAAAA AGCCAGTTCT TGAAGACTGT AAGGTCCCCT TTCGCATCTC AGCATCTGGA GTGTGCAGGG AATTCCTGAC CAGTGGTTTT GCTCCCTCCA ATCCCTTGCC TCCCCCCTCC CATGTTATGC ACTTGTTCTT GGAGAGATGG ACGTTAAAGA AGCGTCAAGC AGTTCTCACT GCAAATAAAT GGTGCAGAAA TAAGAGAGAG AGGATGAAAG CCTAGGAAGT TATAAGTGAT CCTGACCCGA CCCAGCCACC AGGGGGTTAT CTCTTTCCAG GTCCTGCCTT GTGTAGAGTG AGGTGATAAA Y GCTTTAGGCA GCCAAATCCA AGCACAGCTG GGTGCCTGGC GGGGATGGGG TGGGGGTGGT CCTATGTGGT GCCTCTGCCT CTGGAGTTAC CTTTAGGAAA GGTCAAGAGA ACTATCCTCC CCTCCATGTC TGCTGAAAAG GGGCTATTTT GCTAGTCTTG TTATCAGTAA TTCACCACTT AATATAACCA GGTTTTAGGT TTGTATATGA GCGATCCTGG ACATCCAATA CCATCCCCCC AGTTCCCCAG CGCCACTCCT GGACATTCTA GACACCAGCG AGGCTTCTTC TGCAGCCATT CCTAATGTAG CAGATCCATT TTGGGGGGAG TCTGGATGCA GGTGTGTGTG ATCCAGCCTG AATTTGAGAC TCTCAGTTTC TTTAACACCA GCTTGAAAAG TCTGCAATCA CTAGCCCTGA GAGAGTACTT TGGTTCCTAA TGGGATATCC TGAGTCAGGG TGGCTGAAAG AGCTACCAGT TTACCTTGTA CATGGCAGGC >gnl|dbSNP|rs7738545 rs = 7738545|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspec = ?|GMAF = T: 2184:0.4899 GCGTGAGCCA CCGAGCTTGG CCAGTGAAAT GAATTATTAA ACATTATTAA CCTTGGTATT GATATATCAA AATTAATTTA CTAAAGAGTG TTGTGGCCCA CACCTGTAAT CCCAGCACTT TGGGAGGCCA AGGTGAGAGG ATTTCTTGAG CCCAGGAGTT CAAGACCAGC CTGGGCAACA AGGCCAGACC CCATCCCTAC AAAAAATTTT TTTAAAAAAA TAGCCACCAG GTATGGTGGT GCACGCCTGT GGTCTCAACT GCTTGGGAGG CTGAGGCAGG AGGAATGTTT GAGCCCAGAA GGTCGGGGCT GCAGCAGTGA GCTGTGATCA CACCACTGCA TTCCAGCTTG GGTAACAGAG TGAGATCTTT TCTCAAACAA ACAAACAAAC AAACAAAA.AA AAGATTTGGA ATCAATATCC TAGCAAGACT CTGGGTTGCA ACTTTGCAAA TCTTCTGCTG TGCACGTTTG TTGTTGTTGT TGAGACACAG TCTCGCTCTG Y TGCCCAGGCT GGAGTGCAGT GGCACAATCA TTGCTCACTG AAACCTCGAC CTCCTGGACT CAAGCATTCC TCCCGCGTCA GCCTCCCAAG TCTCTGGGAC TATAGGCGTG CACCACCACG CCTGGCTAAT TAAATAAAAA ATTGTGGGTG CCAGGCGCGG TGGCTCACGC CTATAATCCC AGCACTTTGG GAGGGCGAGG AGGGTGGATC ACGAGGTCAA GAGATTGAGA CCATTCTGGC CAACCTGGTG AAATCCAGTC TCTACTAAAA TTACAAAAAT TAGCCGGGCG TGGTGGCGCA TGCCTGTAGT CCCGGCTACT CGGGAGGCTG AGGCAGGAGA ATCACTTGAA GCCGGGAGGC AGAGGTTGCA GTGAGCCGAT ATTGTACCAC TGCACTCCAG CCTGGCGACA GAGCAAGACT TCGTTTCAGA AAAAGAAAAA AAATTTTTTT TTTTTGTAGA AACAGAGTCT TTCTATGTTG CCCAGGCTGA TCGCAAACTC >gnl|dbSNP|rs9295535 rs = 9295535|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = C: 2184:0.2074 TTAAAGGCGT GCACCACCAT GCATGGCTAA TTTTTGTATT TTTAGTAGAG TCAGGATTTC GACGTGTTGG CCAGGCTGGT CTCGAACTCC TGACCTCAGG TAATCCACCC TCCTCGGCCT CCCAAAGTAC TAGGATTACA GGCATGAGCC ACATTACCTG GCCACATTTA AGCTTTTTAA CTAAAAGTTT ATTGGGAGAA TAAAGTGGAG GGCAGTTAAA ATCCCTCTAG TGGAAGAAAA GACCTGGACA ATATGAGCTG TGTTCATTAT GACCACTGGG AAGATGAGTT TCACCCTTCA CATGACTATA TGGTGAGAAC ATGTTTCTCA GCTGAGACAA GATTTCAGGA AAGTTTGCAA GAACCGAGAG AAAATGGAAG AAAATGAAAT ATTTGTTCTT CAGAGTCACC AGTTTTATTA TATGCCTGGA CTCTGTCACT TATGTCAATA AATTTACAAA TGCAAAATAC ACATTTAATT CCAGCGTGGT AGCATACACC Y GTAGTCCTAG ATACTCAGGA GGGTGAGTAT CTAGGACTAC AGGTGTGTGC TACCACGCTT GAACTCAGGA GTTCAAGGCC AGCCTGGACA ACACAGGGAG ACCCCCTCTC TAAATGTATA TACACACATA CACACACACA CACACACACA CACACATTCA AACATTGGAA TCAGATGTCC TGGACAAAAT GCTCAAATCA GCTGAACACT TTGGAATGCT TAACTTTTCT TTTTTTTAGA TGTTTATTTA TTTATTTATT TTGTTTTTTA TTTTATTATT ATTATACTTT AAGTTTTAGG GTACATGTGC GCAATGTGCA GGTTTGTTAC ATATGTATAC ATGTGCCATG TTGGTGTGCT GCACCCATTA ACTCGTCATT TAGCATTAGG TATATCTCCA AATGCTATCC CTCCCCCCTC CCCCCACCCC ACAACAGTCC CCGGAGTGTG ATGTTCCCCT TCCTGTGTCC ATGTGTTCTC ATTGTTCAAT TCCCACCTAT >gnl|dbSNP|rs9295542 rs = 9295542|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/G”|build = 137|suspect = ?|GMAF = G: 2184:0.494 ATATCTCATG TATTGGGTAT CTACTATGTG CCAAGCCTTT TACTAGGTGC TTTACACACA CATTTATATT GTGCCACTTA AAACTTATAG TAACTCTAAA AGATGATGGC TGGGTGCACT GGCCCACACC TGTAATCCCA GCAAGTTGGG AGCCCAAGGT GGGAGGATGG CTTGAGGCCA GGAGTTTGAC ACCAGCAGGG AGAACATAGC AAGACCTCAT CTCTACAAAA TTAAAAAAAA AAATACAAAA ATTAGCCGAG TGTGGTGGTG CACACCTGTA GTCCCAGGTA CTTGGGAGGT TGAGGTGGGA GGATCACTTG AGCCCAGGAG GTTGAGGCTG CAGTGAGCTA TGATTGTACC ACTGCGCTCC AGCTCAGATA ACAGAGCCAG ACCCTATCTC TAAAATTTAA ATAAATAAAT AAATAAATAA ATAAATAAAT AAATAAATAA TGTCTATTAT CCCCATTTTG AAAAAAAAAA AAAATCTGAG AGAAGGCCAG R CACCATGGCT CAAACCTGTA GACAGAGACG GGCAGAAGGC TTGGTCAAGA GTTCGAGACC AGCCTGGCTA ACATGGTGAA AACCCCTGCC TCTACTAAAA ATACAAAAAT TAGCCAAGTA TGATGGTGGC GCCTGCTATC CCAGCTACTT GGGAGGCTGA GGCAGAATTG CTTGAACCCG GGAGGCAGAG GTTGCAGTGA GCTGAGATCA CACCACTGCA CTCCAGCCTG GGTGACAGAG CGAGTATCCA TCTCAAAAAA AAAAAAAAAA AAAAGCCTGA GAGAGAAATC AAGCAATATG CCCCAAATTA CACTACTAGC AAATAACAAA ATCAAAATTC AATCCCAAGT CTCAATTTTC TTTTCAAATT CTTCACTTAC TATATCCGTT TCCTGTTGCT GCTGTAGCAC GTTACTGTGC ACTTTCTCAC GGTGCAGGAG ATGAGAAGTC TGAAATCAAG GAGTCAGCAA GGCCACAGTC CCTCCAGAGG CTCCAGGAGA >gnl|dbSNP|rs9348512 rs = 9348512|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/C”|build = 137|suspect = ?|GMAF = A: 2184:0.2944 ACTAATGCCT CTTCAAATGG AGGGTTTTGT TTGTTTGTTT GTTTATGGGA ATTTTAAGTA ATTTTCAGTG CCTGAGAATG TTCTCCATAA AACCTGTAAC AAAACACATA ATAATGGTTC CAGTGAAAAT AGTTATCTCA AAGTTGGATT GGATTTGAAA TTCTAAATAC CCTATGACTA GGGTATCAAA ATTTAAGGTT TGGTCAAATG TAACTTTTTA GGTGTCTTGT GTATGGTACA AGTTTGAAAG TGTTTATGTG CACTACCTGT TCCATTCATC ATATCTACCC ATATCTGTAT CACTTAAAAT GAATACTTTT AGGTTTATTA AAAAGTAACA CTTCAAGCAA GCAAATGGAA ATTATTTTGC AGTAACTACA AATAATAGAG AAGTATTAAC ATAGAGTTGT GGGCCATGAC CTAGTGGGTT GTTTGTCACT GTTTATTTTC TGCCATTTCC TAGGGGTGAA TTGCATCCTG TACTGTTTAC AGCCTTATCT M CAACTTTTGC AGAGTCAAGA ATTTAAAAGC AGCAGGGCTT GGTGGCCCAT GCCTGTAATC CCAATATTTT GGGAGGCCGC AGCGGGAGGA TCACTTGAGG CCAGGAGTTC CAGACCTCCC TGGGCAACAT GATGAGACCA CATCTCTACA GAAAAATTAG CTGGGCATGC TGGCATGTGC CTGTAGTCCC AGCTCCTCAA GAGGCTGAGG TGGGAGGATC ACTTGAGCCC AGGAGGTCAA GGCTGCAGTG AGCTATGATC ACACCAATGC ACTCTAGCCT GGGGACACAG TGAGACCCTG TCTCAAAAAA AAAAAAAAGA GGAATTTAAA AGCATTTACT ACTATCTAGT GTACTATATA CTTATTATTA GTTTATTTTT GGTCTCCCTC ACCAGAATGT AGGCTCCTTA AAGGCAGTAG TATTGCACAT AATAGGGACT AATATACCTT TATTGAATTA ATTAATGAGG GCCAAGATAT CTCATGTATT GGGTATCTAC >gnl|dbSNP|rs9358529 rs = 9358529|pos = 501|len = 1001|taxid = 9606|mol = =genomic”|class = 1| alleles = =A/C”|build = 137|suspect = ?|GMAF = C: 2184:0.2816 ATTTTTAGTA GAGATGGGGT TTCACCACGT TGGCCAGGCT AGTCTCGATC TCCTGACCTC GTGATCCTCC CACCTCGGCC TCCCCAAAGT GCTGGGATTA CAGGCGTGAG CCACCGAGCT TGGCCAGTGA AATGAATTAT TAAACATTAT TAACCTTGGT ATTGATATAT CAAAATTAAT TTACTAAAGA GTGTTGTGGC CCACACCTGT AATCCCAGCA CTTTGGGAGG CCAAGGTGAG AGGATTTCTT GAGCCCAGGA GTTCAAGACC AGCCTGGGCA ACAAGGCCAG ACCCCATCCC TACAAAAAAT TTTTTTAAAA AAATAGCCAC CAGGTATGGT GGTGCACGCC TGTGGTCTCA ACTGCTTGGG AGGCTGAGGC AGGAGGAATG TTTGAGCCCA GAAGGTCGGG GCTGCAGCAG TGAGCTGTGA TCACACCACT GCATTCCAGC TTGGGTAACA GAGTGAGATC TTTTCTCAAA CAAACAAACA AACAAACAAA M AAAAGATTTG GAATCAATAT CCTAGCAAGA CTCTGGGTTG CAACTTTGCA AATCTTCTGC TGTGCACGTT TGTTGTTGTT GTTGAGACAC AGTCTCGCTC TGCTGCCCAG GCTGGAGTGC AGTGGCACAA TCATTGCTCA CTGAAACCTC GACCTCCTGG ACTCAAGCAT TCCTCCCGCG TCAGCCTCCC AAGTCTCTGG GACTATAGGC GTGCACCACC ACGCCTGGCT AATTAAATAA AAAATTGTGG GTGCCAGGCG CGGTGGCTCA CGCCTATAAT CCCAGCACTT TGGGAGGGCG AGGAGGGTGG ATCACGAGGT CAAGAGATTG AGACCATTCT GGCCAACCTG GTGAAATCCA GTCTCTACTA AAATTACAAA AATTAGCCGG GCGTGGTGGC GCATGCCTGT AGTCCCGGCT ACTCGGGAGG CTGAGGCAGG AGAATCACTT GAAGCCGGGA GGCAGAGGTT GCAGTGAGCC GATATTGTAC CACTGCACTC >gnl|dbSNP/rs9366443 rs = 9366443|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = T: 2184:0.3837 AGTTGTGGGC CATGACCTAG TGGGTTGTTT GTCACTGTTT ATTTTCTGCC ATTTCCTAGG GGTGAATTGC ATCCTGTACT GTTTACAGCC TTATCTCCAA CTTTTGCAGA GTCAAGAATT TAAAAGCAGC AGGGCTTGGT GGCCCATGCC TGTAATCCCA ATATTTTGGG AGGCCGCAGC GGGAGGATCA CTTGAGGCCA GGAGTTCCAG ACCTCCCTGG GCAACATGAT GAGACCACAT CTCTACAGAA AAATTAGCTG GGCATGCTGG CATGTGCCTG TAGTCCCAGC TCCTCAAGAG GCTGAGGTGG GAGGATCACT TGAGCCCAGG AGGTCAAGGC TGCAGTGAGC TATGATCACA CCAATGCACT CTAGCCTGGG GACACAGTGA GACCCTGTCT CAAAAAAAAA AAAAAGAGGA ATTTAAAAGC ATTTACTACT ATCTAGTGTA CTATATACTT ATTATTAGTT TATTTTTGGT CTCCCTCACC AGAATGTAGG Y TCCTTAAAGG CAGTAGTATT GCACATAATA GGGACTAATA TACCTTTATT GAATTAATTA ATGAGGGCCA AGATATCTCA TGTATTGGGT ATCTACTATG TGCCAAGCCT TTTACTAGGT GCTTTACACA CACATTTATA TTGTGCCACT TAAAACTTAT AGTAACTCTA AAAGATGATG GCTGGGTGCA CTGGCCCACA CCTGTAATCC CAGCAAGTTG GGAGCCCAAG GTGGGAGGAT GGCTTGAGGC CAGGAGTTTG ACACCAGCAG GGAGAACATA GCAAGACCTC ATCTCTACAA AATTAAAAAA AAAAATACAA AAATTAGCCG AGTGTGGTGG TGCACACCTG TAGTCCCAGG TACTTGGGAG GTTGAGGTGG GAGGATCACT TGAGCCCAGG AGGTTGAGGC TGCAGTGAGC TATGATTGTA CCACTGCGCT CCAGCTCAGA TAACAGAGCC AGACCCTATC TCTAAAATTT AAATAAATAA ATAAATAAAT >gnl|dbSNP|rs9393239 rs = 9393239|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = T: 2184:0.1625 ATGAGAACTC ACTCACTATC ATGAGGACAG CAAGGGGGAA ATTCTCCCCC ATGAGCCAAT CACCTCCCAC CAGGTCCCTC CCCCAACATT AGGAATTACA ATTTGCATGA GATTTGTGTG GCCACACGGA GCCAAACCAT ATCACATTGG TCAACCTATA TAGTAATGTT TTTCTTAATC TAAATGTATA CTAAGTTAGT GTTTTATCGA TTTAAAAATA CATCTTGAAA AGGATTTTGC AATTTACTTT TTTTTTTTTT TTGTGAGACA GAGTCTCACT CTTGTCCCCC AGGCTGGAGT GTAGTAGCGT GATCTTGGCT CGCTGCAATC TCTGCCTCCC AGGTTCAAGC AATTCTCCTG CCTCAGCCTC CTGAGTAGCT TGGATTACAG GCGCCTGCCA CTACTCCCGG CTAATTTTTT GGTATTTTTA GTAGAGACAG GGTTTCACCA TGTTGGCCAG GCTGGTTTCA AACTCCTGAC CTCAAGTGAT CCGCCCACCT Y GGCTTCCCAA AGTGCTAGGA TTACAGACGT GAGCCACCAT GCCCAGCCCA CAATTTCTTT TAAAACTAAA CATATACAGT TGTCCATCAG TATCTGCAGG GGACTGGTTC CAGGTCTCCC TGCAAGTACC AAAATCACAG ATGCTCAGGA ATCTGATATA AAGCAGGGTA GTACTTGCAT ATAACCCACA CACATCCTCC TGTATACTTT AAATCATCTC TAGATTACTT ATAATACCTA GTACAAGGTG AAGACTATGT AAACAGTTGT TATACTGTAT TTAAAAAAAT AATTTCCACT TTCATTTTAG ATTCAGGTGG TACATATGCA GGTTTGTTAT ATAAGTGTAT TGCATGATGC TGAGGTTTGG AGTTCAATTG ATCCCATCAC TCAGATAGTG AGCATAATTA TACTGTATAC AGTTTAGGAA ATAATAACAA GAAAAAAGTC TATACATATT CCATACAGAC ATGATCATCC TTTCCTCCCA CCCCTTGATT >gnl|dbSNP|rs9460713 rs = 9460713|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = T: 2184:0.1584 CAGACAGGGT CTTGCTCTGT CCCCCAGGCT GGTGTGCATT GGTGTGATCA CCACTCACAG CAGCCTTGAC CTCTGGGGCC CAAGCCATCC TCCCACCTCC GCCTCTGGAG TAGCTGGAAC TACAGGCATG CACCACCATG CCCAGCTAAT ATCAGCCTGC AGTTTTGACT TGGACTTGGA TTTTATAACT GGCTCAACAA AATTATATCC ACTGTTAGTC TGTTTGGGCT ACTATGGCAA AAATACCTGA AACTGGGTGT CTTTTAAACA GAAATTTATT TCTCACAGTT CTGAAGGCTG GGAAGTCCAA GATCAATACC TCAGCTGACT CAGTGTCTGA TGAGGGCCTG TTTCCCAGTT CACAGACAGC CATCTTCTCC CTGTGTCCTC ACATGGTGGA AGGGGCAAGG GAGCTCTCTG CAGTCTCTCT GTAAGGGCAC TAATCCCACT CACAAGTGCT CCACCCTCAT GTCCTAGTCA CCTCCCAAAG GCCCCATCTG Y TAATACCATC ACCTTGGGGA TTAGAATACC AGTGTATGAA TTTGGAGGGG AGATAAGCAT TCAGTCCATT GCACCCTTAT TTCCAAGGCC CAGGGATAAC GCTGAGCTCC TCTGTGGGTG AAGCACATTC AGCTATAAAA CAGTATCTTA AGATTTTCTT CTCGAGTTAG ATTTGGTACG TAGATAACGA CCTTTAACTA TTTGCATCTA TGCAGCTTTT ACTTCCACCT CCTCAACCCA CTGTCTACAA TTCTCACATA GAATTAAGAA TAATTTTGCA TAGCAGATAA TTGGCTGAGC ATGCGGTATT CTTTTGACCC ATTCAAGTGA ATAAAATACT GTATAGGAAC ACTGTCACAA TTTAAATGAA ATTAAATTTC TTGCCCCTTT TCCTCCCCCG ACCATTTAGT CTTTGGGTAG CAACAGAGAT CACTAACATG ATATAACAAT TAATGTAGTT TTGTTTCAGG GCATTAATTT GATACAAATT GTAATTCTGT >gnl|dbSNP|rs9466289 rs = 9466289|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = C: 2184:0.1593 GATTAGAATA CCAGTGTATG AATTTGGAGG GGAGATAAGC ATTCAGTCCA TTGCACCCTT ATTTCCAAGG CCCAGGGATA ACGCTGAGCT CCTCTGTGGG TGAAGCACAT TCAGCTATAA AACAGTATCT TAAGATTTTC TTCTCGAGTT AGATTTGGTA CGTAGATAAC GACCTTTAAC TATTTGCATC TATGCAGCTT TTACTTCCAC CTCCTCAACC CACTGTCTAC AATTCTCACA TAGAATTAAG AATAATTTTG CATAGCAGAT AATTGGCTGA GCATGCGGTA TTCTTTTGAC CCATTCAAGT GAATAAAATA CTGTATAGGA ACACTGTCAC AATTTAAATG AAATTAAATT TCTTGCCCCT TTTCCTCCCC CGACCATTTA GTCTTTGGGT AGCAACAGAG ATCACTAACA TGATATAACA ATTAATGTAG TTTTGTTTCA GGGCATTAAT TTGATACAAA TTGTAATTCT GTTCTCATCA GTTCTGTAAA Y TGCTTTACTG TAATTACACC AAGTATTTGA TCAAATATTG CCGATATTTC CATCTGTTTA GTGTATGGTA TTATGATTTC AAAATTGATT CTAGATTTAG TCCAATAATT TTTAGAATGT CTCTTTCTAT ATAAAAGTCA ATGCAGAAAT AATAATATTT TGAGATAAAA AATAAAGGCA TCTTCTAGTT AATATAACAG ATTTTAGTCA CATTTATATT CTTTAATGTT CAGTGTATGT ATCCACTACA TGAGAAACTC AAGACACAAA CAAAATGGTT ATATTTACTG CAACACTAAG CAATATGGAA CATTAAAAAG AATATGTATT CATAAACAAG ACAAAAGATG GTATAGCAAC ATAAAATTTA CAAGAACATT ATAGCTGGAC TGTATAGGAA GAGCTTGACT GATTTCTTTC CACAATGCAT TTTCATCAAA ACTCATACAT ATTCAGAGAT ACAAATAGCA TACCAGTGAA TTCAATGAAC TGCCACTGAA >gnl|dbSNP|rs9466290 rs = 9466290|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/G”|build = 137|suspect = ?|GMAF = A: 2184:0.1589 CCAGTGTATG AATTTGGAGG GGAGATAAGC ATTCAGTCCA TTGCACCCTT ATTTCCAAGG CCCAGGGATA ACGCTGAGCT CCTCTGTGGG TGAAGCACAT TCAGCTATAA AACAGTATCT TAAGATTTTC TTCTCGAGTT AGATTTGGTA CGTAGATAAC GACCTTTAAC TATTTGCATC TATGCAGCTT TTACTTCCAC CTCCTCAACC CACTGTCTAC AATTCTCACA TAGAATTAAG AATAATTTTG CATAGCAGAT AATTGGCTGA GCATGCGGTA TTCTTTTGAC CCATTCAAGT GAATAAAATA CTGTATAGGA ACACTGTCAC AATTTAAATG AAATTAAATT TCTTGCCCCT TTTCCTCCCC CGACCATTTA GTCTTTGGGT AGCAACAGAG ATCACTAACA TGATATAACA ATTAATGTAG TTTTGTTTCA GGGCATTAAT TTGATACAAA TTGTAATTCT GTTCTCATCA GTTCTGTAAA TTGCTTTACT R TAATTACACC AAGTATTTGA TCAAATATTG CCGATATTTC CATCTGTTTA GTGTATGGTA TTATGATTTC AAAATTGATT CTAGATTTAG TCCAATAATT TTTAGAATGT CTCTTTCTAT ATAAAAGTCA ATGCAGAAAT AATAATATTT TGAGATAAAA AATAAAGGCA TCTTCTAGTT AATATAACAG ATTTTAGTCA CATTTATATT CTTTAATGTT CAGTGTATGT ATCCACTACA TGAGAAACTC AAGACACAAA CAAAATGGTT ATATTTACTG CAACACTAAG CAATATGGAA CATTAAAAAG AATATGTATT CATAAACAAG ACAAAAGATG GTATAGCAAC ATAAAATTTA CAAGAACATT ATAGCTGGAC TGTATAGGAA GAGCTTGACT GATTTCTTTC CACAATGCAT TTTCATCAAA ACTCATACAT ATTCAGAGAT ACAAATAGCA TACCAGTGAA TTCAATGAAC TGCCACTGAA ACCAAAACAT >gnl|dbSNP|rs12175352 rs = 12175352|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = C: 2184:0.2042 AGAATTGTGT TTTACTGTAG TATACCATAC CTTATATGTT TAATAGAACA TATCTCATCT ATATTTTGTA ATTCATTTTT ATAAAACTGC CTGTAAATAG AAAAATTTTA TTATCTCTTT CATGTCTACA GCTTCTACAT TTATGTCCCC TCTTACTTTT TTTTTTTTTG GAGAGACAGT GTCTCACTAT GTTGCCTAGA CCAGTTTCAA ACTCCTGGGC TCAAGTGATC CTTCTGCCTC AGCCTCCCAA AGCGTTGGAA CTACAGGTGT GAGCCAGCCC GCCTGGCCCC TCTTACATTC TTTGTTGTTG TTGTTTTGTT GTTGTTGTCT TTGTTGTTTT TTGAAGCAGA GTCTCCCTCT GTGGCCCAGG CTGGAGTGCA GTGGCTTGAT CGTGGCTCAC TGCAACCTCC GCCTCCCAGG TTCAAGCCAT TCTCCTGCCT CAGCTTCCCG AGTAGCTAGG ACTGTAGGCA TATGCCACCA CGCCCAGCTA ATTTTTTTTA Y AATTTTAGTA GAGATGGGTT TTCACCATGT TGGCCAGGCT GGCCTCAAGC TCCTGACCTC CAGTGATCTT CCTGCCTTGG CCTCCCAAAA TGCTGGGATT ACAGGCATGA GCCACTGGGC CGAGCCCCCT TACATTCTTA ATATAGTTTA TTTGTGTCCT TTCTTTTTTC ACTTTTTCAC TCTTGCTTAC AGGTTTTTCA ATTGTGTTAG GCTTTTTAAA GAACTGCTTG TCTTTGTAAT GCTCTATATT ATAAATTTTA GTTGTATTTA TTTACTTCTG CTCTTACATT AATTTTTCAT TTTTGATCTT TGAATTTATT TTGCTGTTGT TTTCTAATTC TAATTTGGAT GACTAGCTAA TTAAAAAAAA TGTTTTCCCA CTTCTTAAAT GTCAGCATTT ATAGGCTTTT GCGCTATACA TTTCACTCAA AGTAGATTTT GTGAAATGCC ACAATATTTG GCATGTGGCA TTTTCACAAT TGTCAATTCA AAATGTTTTC >gnl|dbSNP|rs12526269 rs = 12526269|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/T”|build = 137|suspect = ?|GMAF = A: 2184:0.2015 AGCTGTGATA TTGTGAATAT AAGAATTGTA CAATTTTCCT TTGAAATGTG CTGCTGAGAA GAATAAAAAT GAAGTTTTCT CTCTGGAAAT GGCTGGAAAC AAACGTCAAA ACCTGACAAT TTACACACAC AGTTCTCTTT CTGACTTGAC TCACCCCTTT GAGAATTACT TTTTAAAACA CCAAAGTTAT AGGATACTAA GTTAATTGTG GTCTTTCTAA TTCTGAAAAA CTGGTTTTCA TTTGCCTCAA GAACTTTTAA GGCAAAGTTG TCTAAGATAC TTCCTTGAAC AAAGACTCAG ACAAAAGCAT TTGACTGCTT TTAATTTCTC AGCATTTTTA CATTTTAAAA CTATAGCTTG AATGAAACTC AAGTGTCCTG AATAAAGAAT AAATACTTAA AAATTGTTTA AATACATATT TTCTCCTTTC ATTGTTGGAG CATTCAAGCA AAGATTGTGT AAAATTCAGG TTAAGTAAAA TGTAAAAAAT ACATATCCAG W TTACATTACA TATTCTTTTT GTGTACAAAT ATTTATACCA ATAAAAACCC CCTAAGATAT TTATATCTTT AACATCTATT TTTCTTTTAC CTTTACTACT AGAAAGAGAA GCTAACAAAG GAAAGCCTCT TCAAAAAATG GGATTTCCTT GGCCTTAGCA GTTCTGGTGT GTTCCACTGC CAACACTAGG TAGGAAAAAT CTGACTTGTG ATGTTGTGAT TAAATAGCGG CCTGGCTCAA ACTGCCCAAA GAGAGGGAAT TCACTATGAG TTCCACAGTT TTATCTTGAG GAAGAAACTA GCAGAAACAC AGAATTTTAG AGCCTTAGAG CTCTCATTAC AAAATGGCCC TCACTATAAA GTGGCATATA CTGAACCCAG CTTAGATGTG TGTACTGAGC CCAGTACAAA TGTATATAGT GAACCCAGAG CTCTCATTAC AAAATGCCTC TCACTATAAA GTGGCATATA CTGAACCCAG CTTAGGTGTG TGTACTGAGC >gnl|dbSNP|rs35076407 rs = 35076407|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = C: 2184:0.4986 TTGAGCCCAG GAGTTCAAGA CCAGCCTGGG CAACAAGGCC AGACCCCATC CCTACAAAAA ATTTTTTTAA AAAAATAGCC ACCAGGTATG GTGGTGCACG CCTGTGGTCT CAACTGCTTG GGAGGCTGAG GCAGGAGGAA TGTTTGAGCC CAGAAGGTCG GGGCTGCAGC AGTGAGCTGT GATCACACCA CTGCATTCCA GCTTGGGTAA CAGAGTGAGA TCTTTTCTCA AACAAACAAA CAAACAAACA AAAAAAAGAT TTGGAATCAA TATCCTAGCA AGACTCTGGG TTGCAACTTT GCAAATCTTC TGCTGTGCAC GTTTGTTGTT GTTGTTGAGA CACAGTCTCG CTCTGCTGCC CAGGCTGGAG TGCAGTGGCA CAATCATTGC TCACTGAAAC CTCGACCTCC TGGACTCAAG CATTCCTCCC GCGTCAGCCT CCCAAGTCTC TGGGACTATA GGCGTGCACC ACCACGCCTG GCTAATTAAA TAAAAAATTG Y GGGTGCCAGG CGCGGTGGCT CACGCCTATA ATCCCAGCAC TTTGGGAGGG CGAGGAGGGT GGATCACGAG GTCAAGAGAT TGAGACCATT CTGGCCAACC TGGTGAAATC CAGTCTCTAC TAAAATTACA AAAATTAGCC GGGCGTGGTG GCGCATGCCT GTAGTCCCGG CTACTCGGGA GGCTGAGGCA GGAGAATCAC TTGAAGCCGG GAGGCAGAGG TTGCAGTGAG CCGATATTGT ACCACTGCAC TCCAGCCTGG CGACAGAGCA AGACTTCGTT TCAGAAAAAG AAAAAAAATT TTTTTTTTTT GTAGAAACAG AGTCTTTCTA TGTTGCCCAG GCTGATCGCA AACTCCTGGG CTCAAGGGAT CCTCTCACCT CCCAAAGTGC TGGGATTACA GGCCTGAGCC ACCTTCCCCA GCCCTATGCA CATTTTCACA AAGATATTTT GAACCCTAGC AGTGGGAAAG GATACTTTTT GACTGGTGAC ATCCCAGAGC >gnl|dbSNP|rs56365413 rs = 56365413|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “C/T”|build = 137|suspect = ?|GMAF = T: 2184:0.1598 TAATGTAACA GGTAGGAAAC ACTCAATATT CGGGAATGCT GATGAGTTTC CTGAGTAGCA TGCGACTGGA ATGGGAAGAA CAGGAATCTG GTATCAGCCT AGCCACAGGC TGGCTGTTAT GTCCTTGGGC AAGTTTCATC TTGTCTCTGG GTTGTATTTC CTGACCTGTA ATGCAAGAGT ATCAGTCCCA ACCATCTCTG AGGCCCTTTC CAGCTCTCAG AGACTCTGGA ATCAGATTCA TACATCTGTC AGCTGAGTTT CCAAACAACA CAGCCTGGAA ACAACAATTC TAAAAATAAA ACATACAACT AAGAAACCAG TCACAAGACT AGAAAACATC ATCACATGTA TTCTGTCACT GGTAACAAAA TAGTTGCATA CATATGCGAG CCAGTCCTAT TTATTATTAT TTTTTTTTTA GAGACAGGGT CTTGCTCTAT TGCCCAGGCT GGAGTGCAGT CACCGCTAAC TGCAGTCTTG AACTCCTGTG CTCCAACGAT Y CTCCTGCCTC AGCTTCCCAA GTAGCTGGGA CCACAGGTGA ACATTGCCAC ACCTGGCTAA TTTTTTATTC TTATTTTTAT TAGAGAAGAG TTCTCCCTTT GTTGCCGAGG CTGGTCTCAA ACTCCTGGTT TCAAGTAATC CTACTGCCTC AGCCTCCCAA AGTGTTGGGG TTACAAGCAC GAGCCACTAT GCCCAGCCAA GTCAATCCTA TTTTAGATTC ATCATTATTC ATTAGAAAAA GTATTTTCAC TTATTTGTAT CTCACCCTAA CTTATAAAAT CCAAATAACG TTTGACTGAC ATGAAGATAA TATGCCACTT AATCATGTTA TTGACTTCAC TTAAGTGGGC TACACAAATA ACGCCAATAA TATTCAGTAA TATAAAAATT ATGACATTTT TCTAAGGGAA CTGAGTCATA CAGGCCTTGA ATGATTATAT ATAATAATCT TTTTTTTTTT TTTTTTTTTT GGACACGGAG TTTTGCTCTT GTTGCCCAGG >gnl|dbSNP|rs75769093 rs = 75769093|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/C”|build = 137|suspect = ?|GMAF = C: 2184:0.429 TCCCATCACT CAGATAGTGA GCATAATTAT ACTGTATACA GTTTAGGAAA TAATAACAAG AAAAAAGTCT ATACATATTC CATACAGACA TGATCATCCT TTCCTCCCAC CCCTTGATTT TTTATTGAAT TCATGAATAT TTGCTGAGTC CATGAATGTG GAACCCACGG ATAGGGAAGG CTGACTGCAC CTACATTATG ACCTGGCAAT TCCACACCTA GGTTACTCAC CCGGGAGAAA TAAAAGCATA TGTCCTCAAA GAGGCTTGTT CAAAAATGTC CATAGCTTTA TTCATAAATA ACTGAAAGCT GAAAACAACC AATAGGAGAA TGAATAAACT AACTGTGGTA AATTCAGACA ATGAAATACT ACACAATAAA AAAGGGAGGA ACCGGCTGGG CGCGGTGGCT CACACCTGTA ATCCCAGCAC ATTGGGAGGC CGAGGTGGGT GGATCACCTG GGGTCAAGAG TTCGAGACCA GCCTGGCCAA CATGGTGAAA M CCCATCTCTA CTAAAAATAC AAAAATAGTC AGGTGTGGTG GCACGCACCT CCAATCCCAG CTACTCGAAA GGCTGAGGCA GGAGAATCAG CTTGAATCCA GGAGGCAGAG GTTGCAGTGA GCTGAGATTG TGCCACTGCA CTCCAGCCTG GGTGACTCTG TCTCAAAAAA AAAGGGGGGG TGGGGGGAGG AACCATTGAT ACATACAACA TCATGATGAA TTCCAAAAAT GTTGTACTGA ATGGAAGAAG CCTTACACAA GAAAGCACAT ACTGTTTATA TATTTATCAT CCTAGAACAA GCAAAACTAA TCTATGGACT AATCTAAGGT GGGGGCAGGG GAATCCAGAG AAAGGGTTAC CTTTGGGAGT TGGGCCATGG GGTTGGGGAC CAGCTGTGCA GGGGAGCTTT CAGGGCAGTC TTAAGGTACT GGATCTCACC AGGGGTTTGC CTTGCTTGTA TACATGGCCT TCCTTGCAAC TTGCTGAATG GCACACTTAT >gnl|dbSNP|rs78113724 rs = 78113724|pos = 501|len = 1001|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/C/G/T”|build = 137|suspect = ?|GMAF = A: 2184:0.2779 TTGCAAACAT TTATTTTCTC ACAGTTCCGG AGGCTGGAAG TCCAAGATGG AGCTGCTGTG AGGGTTGGTT TCCGGTGAGG CCTTTCTTCC TAGCTCGTAG ACAGCCACCT TCTCTCTGTG TCCTCACATG GCTTTTCTTT TGTGTCCATG CCGAGAGAGA GGACTCTCTG AGGACTCTCC CTCTTCTTGT AAGGACACCA GTCCTATCAG ACTAGGGCCC CACTCTTATG ACCTCATTTA ATTTAATTAT GTCTGTAAAG GCCCCTGCTC CAAATATAGT CACATTGAGG ATTATGGCTT CATAATCCTG TGACTCTGGG GAGAGGACAC ATTTCAGTCC ATAACAAAGC CCTTAGTGTG TTTTAGTGCT CAAAACTGTT CATTCATACC TCGGTTATTC CATTATTATT GCCTACGATA TTACCACTTC AGGGTTTTTG TTATTTTTTA CAATATAGAG CACAACGTAT AATAAACTAC ACATACGAAT TCTCATTGAG N AATTACAGAA AATATATCTA TCGCGTCTAA CAAGGTTTAA TTAGCATCTT GGAAAAAAAA AAAAACACCT ACGTTTTTAA GGAAAAAGTT GGCCAATACT GCCATCTGTT GGAATTTTGG TCAAAGCTCA TGTGTTGGAC TTTACTCATT CTTTGTCAAT ATCTTTTCTT TCTTTCTTTC TTTCTTTTTT TCTGAGACGG AGCCTTGCTC TGTTACCCAG GCTGGAGTGT GGTGGCGCGA TCTCGGCTCA CTGCAACCTC CGCCTCCTAG GTTCAAGCGA TTCTCCTGCC TTGGCCTCCT GAGTAGCTGG AATTACAGGC ACGCGCCACC ACGCCCGGCT AATTTTTGTA TTTTTAGTAG AGACGGAGTT TCACCATGTT GGTCAGGCTG GTTTCGAACT CCTGACCTCG TGATCCACCC ACCTCGGCCT CCCAAAGTGC TGGAATTACA GGCGTGAGCC ACCGCGCCCG GCCCTTTGTC AACATCTTAT ATGTTGCTGT >gnl|dbSNP|rs115262601 rs = 115262601|pos = 201|len = 401|taxid = 9606|mol = “genomic”|class = 1| alleles = “A/C”|build = 132|suspect = ?|GMAF = C: 2184:0.006 GTGAGAGACA GAGTCACAAA GAGAAAGAGA CAGTGAGGGG CCAGAACGAC TCTCTTTTCT CCGATTGTCA ATGCCCAGTG GGAGCCGGGA GCCCAACAGG CCCAGCCCAT CAGATTCGGC CCCTCCGGGC CCCAAATCCG CTCGCCCCAC CCGAGATCCA GGCCTCCAGC CACTTGCCTA ACTGTGAGCC CGCAAGAGCC M GGCCCGCGGC TCCCTCCTTC CTCCTCCTGC GGCAGTCTCG CGGCTTTCAA ACCTTAGTCG AACCCACAGA AGGCCCAGTC CCAGGCCAAA CCTACTCAAC AGGCACCTTC TCACGGCCTA GGAATTCTGC AGCGAAATTC ACTGGAATTT GAGGAGAAAA CCCAAAGACT GCTCCGAAAG GACTCCCCCA GTCTTCAGCC >gnl|dbSNP|rs184577|allelePos = 501|totalLen = 1001|taxid = 9606|snpclass = 1|alleles = ‘C/T’|mol = Genomic|build = 137 CTATGGAAGT TTCTCAAAAA ATTAAAAATA GAACTACCAT GTGATCCAGA AATCCCACTG CTGGGTATTT ATCCAAAGGA AAAAAAATCA ATATATCAAA GGGAGACCTG CACTCCCATG TTTATTGCAG CACTATTCAC AATAGCCAAG ATACAGTATC AATCTAAGTG TCCATCAACA GATGAATGGA TAAAGCAAAT GTGATACACA CACACACACA CACACACACA CACACACACA ATGGAATACT ATTAAGCCGT AAAAAAGAAT GAAATTCTAT CATTTGCAGG AACATGTATG GAATTGAAGG GCATCTTGTT AAGTAAAATC AGCCAGGCAC CGAAAGACAA ATATTGCATA TTCTTACTCA TATGTGGGAG CTAAAAAGAT GGATCTCATG GAGGTAGAGA AAAGAATGGT AGCTACTAGA GGCTATGAAG GGTGTGTGGG ATGAAGAGAG GTTGGTTAAT AGGTACAGAC ATATAGTTAG ACGGAATAAG Y GCTAGTATTC AGCCTCAAAG TAGGGTGACT ATAGTTAACA AAAACATATT GAGTATCTCA AAATAGCCAG AAGAGAAAAT TTGAAATGTT CCTAGCTCAA AGAAATGATA CATGTTCAAG GCGATGGATA TCCTAAATAC CCTGATTTGA TCATTACACA TTCTATGGCT GTGTCAAGAT ATCACAGATA CCCCATAAAT ATGTGTAATT ATTATGTATC AAAAACTTTA TATAAAAAAC ATTAATTTGC TGTATTTTTG ATTCTACAAT TGGGCAGCAC TTTATTCCAT AAAATAGAAT GAGTGTTCTG ATGAGCCAAG AAGAGGAGGT TGGTTTTACA GACAGAAAAG GGCTGTGGAA AGCAGAAAAA AACAAACAAA AAAAATGTGG ATTGGTCATT TCAAAGTTTC TTTTCATGTA AAGGTTAAAG CAGAGGGGAC TTTCTTGTCC TGCTGGCACT GGATCTAGGT CAGTGTTGGA GACGATGTCT GGGACTCAGG >gnl|dbSNP|rs619373|allelePos = 61|totalLen = 121|taxid = 9606|snpclass = 1|alleles = ‘A/G’|mol = Genomic|build = 137 TTTGGGGGCT GAGTGTTCTT CCAATCCTAA AAACAACTCT CTGTGGCCAA ATTGCTCCAG R CCTCCAACAT ATCCAACATA GCTATATGTG AATAGAGTCA TCAGCTTCTG CTGTTCGTAT ADDITIONAL NOTES ABOUT TABLE 7: Global Minor Allele Frequency (GMAF) [ie. G: 0.262:330 <-- (allele:count:frequency)] (http://www.ncbi.nlm.nih.gov/projects/SNP/docs/rs_attributes.html#gmaf) “G: 0.262:330”. This means that for this rs, minor allele is ‘G’ and has a frequency of 26.2% in the 1000Genome phase 1 population and that ‘G’ is observed 330 times in the sample population of 629 people (or 1258 chromosomes).

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Various references are cited herein, the contents of which are hereby incorporated by reference in their entireties. 

What is claimed is:
 1. A kit for determining whether a subject has an increased risk of having or developing breast cancer comprising a means for detecting a 6p24 SNP biomarker.
 2. The kit according to claim 1 wherein the 6p24 SNP biomarker is rs9348512 SNP.
 3. The kit according to claim 1 further comprising a means for detecting a biomarker of BRCA2.
 4. The kit according to claim 1 further comprising a means for detecting one or more biomarkers selected from the group consisting of 10q26 (FGFR2), 16q12 (TOX3), 12p11 (PTHLH), 5q11 (MAP3K1), 9p21 (CDKN2A/B), 11p15 (LSP1), 8q24, 20q13, 6q25 (ESR1), 10q21 (ZNF365), 3p24 (SLC4A7, NEK10), 12q24, 5p12 and 11q13.
 5. The kit according to claim 1 further comprising a means for detecting one or more biomarkers selected from the group consisting of: (a) a biomarker of 10q26 (FGFR2) which is the SNP rs2420946; (b) a biomarker of 16q12 (TOX3) which is the SNP rs3803662; (c) a biomarker of 12p11 (PTHLH) which is the SNP rs27633; (d) a biomarker of 5q11 (MAP3K1) which is the SNP rs16886113; (e) a biomarker of 10q26 (CDKN2A/B) which is the SNP rs10965163; (f) a biomarker of 8q24 which is the SNP rs4733664; (g) a biomarker of 6q25 (ESR1) which is the SNP rs2253407; and (h) a biomarker of 10q21 (ZNF365) which is the SNP rs17221319.
 6. A method for assessing the likelihood that a subject has or will develop breast cancer comprising determining whether the subject carries a 6p24 SNP biomarker and a BRCA2 biomarker, where the presence of both biomarkers indicates that while the subject has an increased risk of having or developing breast cancer relative to the general population, the risk is less than if the 6p24 biomarker were absent.
 7. The method of claim 6, wherein the subject has previously been tested and known to carry a BRCA2 biomarker.
 8. The method of claim 6, wherein the 6p24 SNP biomarker is rs9348512 SNP.
 9. The method of claim 6, further comprising determining whether the subject carries one or more auxiliary biomarkers selected from the group consisting of 10q26 (FGFR2), 16q12 (TOX3), 12p11 (PTHLH), 5q11 (MAP3K1), 9p21 (CDKN2A/B), 11p15 (LSP1), 8q24, 6q25 (ESR1), 10q21 (ZNF365), 3p24 (SLC4A7, NEK10), 12q24, 5p12, and 11q13.
 10. The method of claim 6, wherein the presence of the biomarker is determined in a sample taken from the subject.
 11. The method of claim 6 comprising the further step, where the subject is found to carry a 6p24 SNP, of recommending or performing regular breast screening to monitor for the presence of cancer.
 12. The method of claim 11 wherein screening is performed by a clinical breast exam, biopsy, mammography, ultrasound, magnetic resonance imaging, or similar techniques.
 13. The method of claim 6 comprising the further step, where the subject is found not to carry the 6p24 SNP, of recommending or performing a mastectomy or oophorectomy, or recommending or administering anti-estrogen therapy or chemoprevention.
 14. A method of treating a subject who carries a BRCA2 biomarker, comprising determining whether the subject carries a 6p24 SNP biomarker and, where the 6p24 SNP biomarker is absent, advising the subject that she is at high risk for developing breast cancer relative to a subject carrying both the 6p24 SNP and BRCA2 biomarkers and to the general population.
 15. The method of claim 14, wherein the subject has previously been tested and known to carry a BRCA2 biomarker.
 16. The method of claim 14, wherein the 6p24 SNP biomarker is rs9348512 SNP.
 17. The method of claim 14, further comprising determining whether the subject carries one or more auxiliary biomarkers selected from the group consisting of 10q26 (FGFR2), 16q12 (TOX3), 12p11 (PTHLH), 5q11 (MAP3K1), 9p21 (CDKN2A/B), 11p15 (LSP1), 8q24, 6q25 (ESR1), 10q21 (ZNF365), 3p24 (SLC4A7, NEK10), 12q24, 5p12 and 11q13.
 18. The method of claim 14, wherein the presence of the biomarker is determined in a sample taken from the subject.
 19. The method of claim 14 comprising the further step, where the subject is found not to carry the 6p24 SNP, of recommending or performing a mastectomy or oophorectomy, or recommending or administering anti-estrogen therapy or chemoprevention. 